Ecological risk assessment method and system for heavy metal contaminated soil based on multi-source analysis

Abstract

The invention discloses a method and system for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis, and specifically relates to the technical field of soil pollution monitoring. Field sampling data and remote sensing data in a heavy metal contaminated soil assessment area are obtained to determine whether the spatial resolutions of the two sets of data match. When the spatial resolutions of the two sets of data do not match, the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain features are respectively evaluated. A comprehensive analysis is performed to determine the degree of influence of consistency of assessment results in the assessment area. The degree of influence of the assessment area is classified to identify high-interference assessment areas and low-interference assessment areas. The degree of influence of consistency of assessment results in the high-interference assessment area is further analyzed, and according to the analysis results, an early prediction and warning is performed before a risk event occurs in the heavy metal contaminated soil, so as to ensure timely response and effective management of ecological risks of the heavy metal contaminated soil.

Description

Technical Field

[0001] The present invention relates to the technical field of soil pollution monitoring, and in particular to a method and system for ecological risk assessment of heavy metal polluted soil based on multi-source analysis. Background Art

[0002] Ecological risk assessment of heavy metal contaminated soil based on multi-source analysis refers to a method that comprehensively utilizes multiple means such as field sampling, laboratory analysis, remote sensing technology and geographic information systems to fully understand the heavy metal pollution in the soil and its possible harm to the ecosystem. Through this assessment, we can deeply analyze the distribution characteristics and migration and transformation laws of heavy metals in the soil, and then predict their potential impact on vegetation, soil microorganisms, groundwater and ecological chains, providing a scientific basis for environmental managers to formulate effective soil remediation and ecological protection strategies to ensure the health and sustainable development of the soil ecosystem.

[0003] Remote sensing technology is a technology that obtains surface information through long-distance sensors such as satellites or aircraft. It can provide land cover information over a large area and identify some surface features, such as vegetation cover, land use type, etc. In the assessment of soil heavy metal pollution, remote sensing technology can be used to detect the general properties and surface characteristics of the land, for example, to identify the vegetation status and land use type in the polluted area, so as to preliminarily infer the possible heavy metal pollution areas.

[0004] The shortcomings of the prior art:

[0005] In existing technologies, field sampling data is usually obtained by collecting heavy metal contaminated soil samples at specific locations, so its spatial coverage is relatively limited. Remote sensing technology can provide a wide range of land coverage, but its resolution may not be able to capture subtle soil changes. If the spatial coverage of the two sets of data does not match, it may lead to inconsistent assessment results. At the same time, if the deviation of the assessment results leads to the failure to detect or deal with soil pollution problems in a timely manner, it may cause risk management measures to lag behind or be insufficient, and fail to effectively deal with soil pollution problems, thereby increasing the risk of environmental and human health being affected. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis to overcome the shortcomings of the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: A method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis, comprising the following steps:

[0008] S1: Obtain field sampling data and remote sensing data in the heavy metal contaminated soil assessment area, analyze the overlapping parts of the two sets of data, and determine whether the spatial resolutions of the two sets of data match;

[0009] S2: When the spatial resolutions of the two sets of data do not match, obtain the density of the field sampling points in the assessment area, compare the density of the field sampling points with the pixel resolution of the remote sensing image, determine the degree of abnormality of the spatial distribution density of the field sampling points, and evaluate the rationality of the distribution density of the field sampling points;

[0010] S3: Analyze the heavy metal pollution in different soil types in the assessment area, determine the deviation of heavy metal pollution data in different levels of complexity according to the complexity of the terrain characteristics in the assessment area, and evaluate the accuracy of heavy metal pollution data under different terrain characteristics;

[0011] S4: Comprehensively analyze the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics to determine the impact of the consistency of the assessment results in the assessment area;

[0012] S5: Based on the assessment results, the impact degree of the assessment area is classified and high interference assessment areas and low interference assessment areas are identified;

[0013] S6: Further analyze the impact of the consistency of assessment results in the high-interference assessment area, and make early predictions and warnings before risk events occur in heavy metal contaminated soil based on the analysis results.

[0014] In a preferred embodiment, in S1, the spatial coverage overlap of the two sets of data is analyzed to determine whether the spatial resolutions of the two sets of data match, specifically:

[0015] Count the number of field sampling points and remote sensing pixels in the overlapping area, calculate the average sampling point density and pixel resolution in the overlapping area, and analyze the matching between the two; calculate the average number of sampling points corresponding to each remote sensing pixel: average number of sampling points per pixel = N / M; N is the number of field sampling points, and M is the total pixel area;

[0016] A comparative analysis is performed on the average number of sampling points per pixel. If the average number of sampling points per pixel is equal to 1, it indicates that the density of field sampling points and the remote sensing pixel resolution are matched; if the average number of sampling points per pixel is greater than 1, it is calibrated as a mismatch; if the average number of sampling points per pixel is less than 1, it is calibrated as a mismatch.

[0017] In a preferred embodiment, in S2, the density of the field sampling points is compared with the pixel resolution of the remote sensing image to determine the average number of sampling points contained in each remote sensing pixel. The density drift index of the sampling point distribution is obtained according to the uniformity of the spatial distribution of the field sampling points and the remote sensing image to evaluate the rationality of the field sampling point distribution density. The density drift index is obtained as follows:

[0018] Obtain the field sampling point data and remote sensing data in the assessment area, divide the assessment area into Q pixel units, count the number of field sampling points contained in each pixel, obtain the sampling point density distribution, and mark it as x i ;

[0019] After summing the density of each sampling point, calculate its ratio to the total number of pixel units, that is, calculate the global average sampling point density, marked as X, and construct the spatial weight matrix W through the adjacency relationship ij , represents the spatial relationship between each pixel unit, i, j are adjacent pixel units, and the density drift index is calculated. The specific calculation expression is: Where LGS is the density drift index, x j For x i Neighboring density distribution of sampling points.

[0020] In a preferred embodiment, the obtained density drift index is compared with the standard density value of the spatial distribution of the field sampling points and the remote sensing image. If the density drift index is greater than or equal to the standard density value, an abnormal distribution density signal of the field sampling points is generated; if the density drift index is less than the standard density value, a normal distribution density signal of the field sampling points is generated.

[0021] In a preferred embodiment, in S3, the deviation of heavy metal pollution data in different complexity levels is judged, and the pollution migration variation index of heavy metal pollution data is obtained according to the heavy metal migration and accumulation patterns under different terrain categories, and the accuracy of heavy metal pollution data under different terrain characteristics is evaluated. The method for obtaining the pollution migration variation index is:

[0022] Obtain the heavy metal pollution data and terrain feature data in the assessment area, including the spatial location information of different terrain categories; use the kernel density estimation method to estimate the density of heavy metal pollution data under each terrain category, including using the Gaussian kernel function to calculate the kernel density of heavy metal pollution data for each terrain category. The specific calculation expression is: In the formula, K(s) is the kernel density of heavy metal pollution data, s is the spatial position, and the kernel density estimation value of each terrain category is calculated according to the calculated kernel density value of heavy metal pollution data. The specific calculation expression is: Where n is the number of samples, h is the bandwidth parameter, d is the spatial dimension, and s isa is the location of the sample point, a is the sample number, and F(s) is the kernel density estimate for each terrain category;

[0023] For each terrain category, the semivariogram of heavy metal pollution data is calculated according to the calculated kernel density estimate of each terrain category, and the semivariogram under each terrain category is drawn to observe its changing trend. The change of heavy metal pollution degree between different points is analyzed according to the fitted semivariogram, and the pollution migration variation index is calculated. The specific calculation expression is: Where KG is the pollution migration variation index.

[0024] In a preferred embodiment, the calculated pollution migration variation index is compared with the heavy metal pollution migration threshold between different points in the historical data. If the pollution migration variation index is greater than or equal to the pollution migration threshold, an abnormal signal of heavy metal pollution data accuracy is generated, the sampling and evaluation scheme is adjusted, the sampling density of high error areas is increased, and more accurate remote sensing and analysis techniques are used; if the pollution migration variation index is less than the pollution migration threshold, a normal signal of heavy metal pollution data accuracy is generated without additional processing.

[0025] In a preferred embodiment, in S4, the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain features are comprehensively analyzed, specifically:

[0026] The density drift index and the pollution migration variation index are normalized, and the interference coefficient of the consistency of the assessment results of the assessment area is calculated by the normalized density drift index and the pollution migration variation index.

[0027] In a preferred embodiment, in S5, the impact degree of the assessment area is classified to identify the high interference assessment area and the low interference assessment area, specifically:

[0028] The interference coefficient of the degree of influence of the consistency of the assessment results of the obtained assessment area is compared with the interference threshold. If the interference coefficient of the degree of influence of the consistency of the assessment results of the assessment area is greater than or equal to the interference threshold, an assessment abnormality signal is generated and the corresponding assessment area is marked as a high-interference assessment area; if the interference coefficient of the degree of influence of the consistency of the assessment results of the assessment area is less than the interference threshold, no assessment abnormality signal is generated and the corresponding assessment area is marked as a low-interference assessment area.

[0029] In a preferred embodiment, when the assessment area is marked as a high-interference assessment area, that is, the interference coefficient of the degree of influence of the consistency of the assessment results of the assessment area within a period of time is greater than or equal to the interference threshold, the interference coefficient of the degree of influence of the consistency of the assessment results of the assessment area within a subsequent period of time is continuously collected, and the collected interference coefficients greater than the interference threshold are used to establish a data set, and the interference coefficients in the data set are compared and analyzed with the interference threshold to calculate the risk coefficient of heavy metal contaminated soil;

[0030] The obtained ecological risk coefficient is compared with the ecological risk coefficient reference threshold. If the ecological risk coefficient is greater than or equal to the ecological risk coefficient reference threshold, a warning signal is generated; if the ecological risk coefficient is less than the ecological risk coefficient reference threshold, no warning signal is generated.

[0031] The present invention also provides a heavy metal contaminated soil ecological risk assessment system based on multi-source analysis, including a resolution judgment module, a sampling point density analysis module, a pollution data accuracy judgment module, a comprehensive analysis module, an assessment area division module and a prediction and early warning module;

[0032] Resolution judgment module: obtains field sampling data and remote sensing data in the heavy metal contaminated soil assessment area, analyzes the overlapping parts of the two sets of data, and determines whether the spatial resolutions of the two sets of data match;

[0033] Sampling point density analysis module: When the spatial resolutions of the two sets of data do not match, the density of the actual sampling points in the assessment area is obtained, and the density of the actual sampling points is compared with the pixel resolution of the remote sensing image to determine the degree of abnormality of the spatial distribution density of the actual sampling points and evaluate the rationality of the distribution density of the actual sampling points;

[0034] Pollution data accuracy judgment module: Analyze the heavy metal pollution in different soil types in the assessment area, judge the deviation of heavy metal pollution data in different complexities according to the complexity of the terrain characteristics in the assessment area, and evaluate the accuracy of heavy metal pollution data under different terrain characteristics;

[0035] Comprehensive analysis module: Comprehensively analyze the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics to determine the impact of the consistency of the assessment results in the assessment area;

[0036] Assessment area division module: Based on the assessment results, the impact degree of the assessment area is classified and high interference assessment areas and low interference assessment areas are identified;

[0037] Prediction and warning module: further analyze the impact of consistency of assessment results in high-interference assessment areas, and make early predictions and warnings before risk events occur in heavy metal contaminated soil based on the analysis results.

[0038] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0039] 1. The present invention makes full use of the combination of field sampling data and remote sensing data to make up for the lack of spatial coverage mismatch between the two sets of data. First, by analyzing and comparing the data, the spatial distribution density of field sampling points and the accuracy of the data can be accurately evaluated, thereby improving the consistency and accuracy of the evaluation results. Secondly, by dividing the evaluation area into high-interference and low-interference evaluation areas, and taking timely early warning measures for the high-interference evaluation areas, the risks caused by data inconsistency and deviation can be effectively reduced, and the safety of the environment and human health can be guaranteed.

[0040] 2. The present invention can improve the accuracy and timeliness of ecological risk assessment of heavy metal contaminated soil by utilizing multi-source data, comprehensive analysis and early warning mechanism. Through timely early warning and effective management, the risks to the environment and human health can be reduced, providing strong support for the governance of soil pollution problems and achieving sustainable development of the ecological environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0042] Figure 1 The present invention is a flow chart of the method.

[0043] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Example 1

[0046] See also Figure 1As shown, the method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis described in this embodiment includes the following steps:

[0047] S1: Obtain field sampling data and remote sensing data in the heavy metal contaminated soil assessment area, analyze the overlapping parts of the two sets of data, and determine whether the spatial resolutions of the two sets of data match;

[0048] S2: When the spatial resolutions of the two sets of data do not match, obtain the density of the field sampling points in the assessment area, compare the density of the field sampling points with the pixel resolution of the remote sensing image, determine the degree of abnormality of the spatial distribution density of the field sampling points, and evaluate the rationality of the distribution density of the field sampling points;

[0049] S3: Analyze the heavy metal pollution in different soil types in the assessment area, determine the deviation of heavy metal pollution data in different levels of complexity according to the complexity of the terrain characteristics in the assessment area, and evaluate the accuracy of heavy metal pollution data under different terrain characteristics;

[0050] S4: Comprehensively analyze the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics to determine the impact of the consistency of the assessment results in the assessment area;

[0051] S5: Based on the assessment results, the impact degree of the assessment area is classified and high interference assessment areas and low interference assessment areas are identified;

[0052] S6: Further analyze the impact of the consistency of assessment results in the high-interference assessment area, and make early predictions and warnings before risk events occur in heavy metal contaminated soil based on the analysis results.

[0053] Among them, in S1, the field sampling data and remote sensing data in the heavy metal contaminated soil assessment area are obtained, and the spatial coverage overlap of the two sets of data is analyzed to determine whether the spatial resolutions of the two sets of data match. Specifically:

[0054] Field sampling data: including the location of the sampling point (latitude and longitude coordinates), heavy metal concentration values, etc. Remote sensing data: including image data (such as satellite images), heavy metal concentration distribution, etc.

[0055] Ensure that field sampling data and remote sensing data use the same spatial reference system (such as WGS84). Clean the data to remove outliers and noise to ensure data quality.

[0056] Use GIS software (such as ArcGIS, QGIS) or programming tools (such as Geopandas in Python) to load the field sampling data and remote sensing data onto the same map. Determine the overlapping areas of the field sampling data and remote sensing data. You can find the common coverage by constructing a bounding box of the two sets of data. In the overlapping area, extract the field sampling points and the corresponding remote sensing pixels.

[0057] Calculate the density of sampling points in the field (the number of sampling points per unit area). For example, count the number of sampling points per square kilometer in the overlapping area.

[0058] Get the pixel resolution of the remote sensing data (the actual ground area represented by each pixel). For example, the resolution of a remote sensing image may be 30 meters, which means that each pixel represents a ground area of ​​30 meters by 30 meters.

[0059] Compare the density of field sampling points to the pixel resolution of the remote sensing data. For example, if the field sampling point density is 100 points per square kilometer, and the resolution of the remote sensing data is 30 meters per pixel, there may be less than 1 sampling point in each pixel, indicating a resolution mismatch between the two.

[0060] Make a resolution matching map, visualize the sampling points and pixel grids in the overlapping area, and intuitively display the distribution of the two; count the number of field sampling points and remote sensing pixels in the overlapping area, calculate the average sampling point density and pixel resolution in the overlapping area, and analyze the matching between the two; calculate the average number of sampling points corresponding to each remote sensing pixel: the average number of sampling points per pixel = N / M; N is the number of field sampling points, and M is the total pixel area;

[0061] A comparative analysis is performed on the average number of sampling points per pixel. If the average number of sampling points per pixel is equal to 1, it indicates that the density of field sampling points and the remote sensing pixel resolution are matched; if the average number of sampling points per pixel is greater than 1, it indicates that the density of field sampling points is too high, which may be too dense relative to the remote sensing data resolution, and it is calibrated as a mismatch; if the average number of sampling points per pixel is less than 1, it indicates that the density of field sampling points is too low, which may be too sparse relative to the remote sensing data resolution, and it is calibrated as a mismatch.

[0062] S2: When the spatial resolutions of the two sets of data do not match, obtain the density of the field sampling points in the assessment area, compare the density of the field sampling points with the pixel resolution of the remote sensing image, determine the degree of abnormality of the spatial distribution density of the field sampling points, and evaluate the rationality of the distribution density of the field sampling points.

[0063] Sampling point density refers to the number of field sampling points collected per unit area, usually expressed as points / square kilometer or points / square mile. The acquisition method includes: determining the total area of ​​the assessment area (for example, square kilometers). Counting the total number of field sampling points in the assessment area. Sampling point density = total number of sampling points / total area of ​​the assessment area. The resolution of remote sensing images is usually expressed as the ground area represented by each pixel (for example, 30 meters × 30 meters).

[0064] The density of field sampling points is compared with the pixel resolution of the remote sensing image to determine the average number of sampling points contained in each remote sensing pixel. The density drift index of the sampling point distribution is obtained according to the uniformity of the spatial distribution of the field sampling points and the remote sensing image to evaluate the rationality of the field sampling point distribution density. The density drift index is obtained as follows:

[0065] Obtain the field sampling point data and remote sensing data in the assessment area, divide the assessment area into Q pixel units, the pixel unit needs to be consistent with the pixel of the remote sensing image, count the number of field sampling points contained in each pixel, obtain the sampling point density distribution, and mark it as x i ;

[0066] After summing the density of each sampling point, calculate its ratio to the total number of pixel units, that is, calculate the global average sampling point density, marked as X, and construct the spatial weight matrix W through the adjacency relationship ij , represents the spatial relationship between each pixel unit, i, j are adjacent pixel units, and the density drift index is calculated. The specific calculation expression is: Where LGS is the density drift index, x j For x i The adjacent density distribution of sampling points;

[0067] The obtained density drift index is compared with the standard density value of the spatial distribution of the field sampling points and the remote sensing image. If the density drift index is greater than or equal to the standard density value, it means that there is an abnormality in the uniformity of the spatial distribution of the field sampling points and the remote sensing image, and the lower the rationality of the field sampling point distribution density, the more an abnormal signal of the field sampling point distribution density is generated; if the density drift index is less than the standard density value, it means that there is no abnormality in the uniformity of the spatial distribution of the field sampling points and the remote sensing image, and the higher the rationality of the field sampling point distribution density is, the more a normal signal of the field sampling point distribution density is generated.

[0068] Ideally, field sampling points should be evenly distributed throughout the assessment area to ensure that the soil contamination in each area is fully reflected. If the density drift index is large, it means that the spatial distribution uniformity of the sampling points is poor, resulting in inaccurate assessments in some areas.

[0069] Evenly distributed sampling points can provide more comprehensive and accurate assessment results, reflecting the soil pollution status of the entire area. When the density drift index is large, the assessment results may be biased towards high-density sampling areas, while the pollution conditions in low-density or unsampled areas may be ignored, affecting the comprehensiveness of the assessment.

[0070] The resource allocation and governance strategies formulated based on the assessment results depend on the accuracy and rationality of the assessment. When the density drift index is large, inaccurate assessment results may lead to unreasonable resource allocation, such as investing too many resources in certain low-risk areas and ignoring the governance needs of high-risk areas, thus affecting the effectiveness of pollution control.

[0071] S3: Analyze the heavy metal pollution in different soil types within the assessment area, determine the deviation of heavy metal pollution data within different levels of complexity according to the complexity of the terrain characteristics within the assessment area, and evaluate the accuracy of heavy metal pollution data under different terrain characteristics.

[0072] Soil samples are collected from various locations within the assessment area and laboratory analyzed to determine the heavy metal content in different soil types. The assessment area is divided into different soil types, such as clay, sand, loam, etc. The characteristics of each soil type affect the adsorption and migration of heavy metals. The heavy metal content in different soil types is compared to identify the soil types with more serious pollution.

[0073] Use topographic mapping data or remote sensing technology to obtain the topographic features of the assessment area, including slope, altitude, and undulations. Divide the terrain into different categories according to its complexity, such as plains, hills, and mountains. Complex terrain often includes more ups and downs and changes. Analyze the impact of terrain on heavy metal pollution. For example, areas with larger slopes may cause heavy metal migration due to water erosion, and valleys may accumulate more pollutants due to the gathering of water bodies.

[0074] To determine the deviation of heavy metal pollution data at different levels of complexity, the pollution migration variation index of heavy metal pollution data is obtained according to the heavy metal migration and accumulation patterns under different terrain categories, and the accuracy of heavy metal pollution data under different terrain characteristics is evaluated. The method for obtaining the pollution migration variation index is as follows:

[0075] Obtain the heavy metal pollution data and terrain feature data in the assessment area, including the spatial location information of different terrain categories; use the kernel density estimation method to estimate the density of heavy metal pollution data under each terrain category, including using the Gaussian kernel function to calculate the kernel density of heavy metal pollution data for each terrain category. The specific calculation expression is: In the formula, K(s) is the kernel density of heavy metal pollution data, s is the spatial position, and the kernel density estimation value of each terrain category is calculated according to the calculated kernel density value of heavy metal pollution data. The specific calculation expression is: Where n is the number of samples, h is the bandwidth parameter, d is the spatial dimension, and s is a is the location of the sample point, a is the sample number, and F(s) is the kernel density estimate for each terrain category;

[0076] For each terrain category, the semivariogram of heavy metal pollution data is calculated based on the calculated kernel density estimate of each terrain category; the semivariogram describes the spatial correlation between different points, that is, the change in the correlation between variables with the increase of spatial distance, and selects a suitable semivariogram model (such as spherical model, exponential model, Gaussian model, etc.), and determines its parameters through fitting;

[0077] Draw the semivariogram for each terrain category and observe its changing trend. Analyze the changes in the heavy metal pollution degree between different points based on the fitted semivariogram and calculate the pollution migration variation index. The specific calculation expression is: Where KG is the pollution migration variation index.

[0078] The calculated pollution migration variation index is compared with the heavy metal pollution migration threshold between different points in the historical data. If the pollution migration variation index is greater than or equal to the pollution migration threshold, it means that the accuracy of the heavy metal pollution data under different terrain features is lower. At this time, an abnormal signal of the accuracy of the heavy metal pollution data is generated, and the sampling and evaluation schemes are adjusted, the sampling density in high error areas is increased, and more accurate remote sensing and analysis techniques are used. If the pollution migration variation index is less than the pollution migration threshold, it means that the accuracy of the heavy metal pollution data under different terrain features is higher. At this time, a normal signal of the accuracy of the heavy metal pollution data is generated without additional processing.

[0079] A higher pollution transport variation index may mean that the data are less consistent across different terrain features, and therefore less accurate for heavy metal pollution.

[0080] The increase in the pollution transport variability index may reflect the increased complexity in the environment, such as the diversity of terrain features, differences in soil types, etc., which will increase the difficulty and uncertainty of the assessment.

[0081] A larger pollution transport variation index means that the pollution situation in the assessment area has a higher spatial variability, so more surveys and monitoring are needed to obtain more comprehensive and accurate data to support the formulation of pollution control and remediation decisions.

[0082] S4: Comprehensively analyze the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics to determine the impact of the consistency of the assessment results in the assessment area

[0083] The density drift index and the pollution migration variation index are normalized, and the interference coefficient of the consistency of the assessment results of the assessment area is calculated by the normalized density drift index and the pollution migration variation index.

[0084] For example, the present invention may use the following formula to calculate the interference coefficient of the consistency influence degree of the evaluation result of the evaluation area, and the calculation expression is: Where WP is the interference coefficient, LGS is the density drift index, KG is the pollution migration variation index, a1 and a2 are the proportional coefficients of the density drift index and the pollution migration variation index, respectively, and a2>a1>0.

[0085] S5: Based on the assessment results, the impact levels of the assessment areas are classified, and high-interference assessment areas and low-interference assessment areas are identified.

[0086] The obtained interference coefficient of the degree of influence on the consistency of the assessment results of the assessment area is compared with the interference threshold. If the interference coefficient of the degree of influence on the consistency of the assessment results of the assessment area is greater than or equal to the interference threshold, it means that the greater the degree of interference to the consistency of the assessment results of the assessment area, the higher the possibility of deviation in the assessment results. At this time, an assessment abnormality signal is generated, and the corresponding assessment area is marked as a high-interference assessment area; more in-depth field investigation and verification are needed.

[0087] If the interference coefficient of the consistency of the evaluation results of the evaluation area is less than the interference threshold, it means that the consistency of the evaluation results of the evaluation area is less disturbed, and the possibility of deviation in the evaluation results is lower. At this time, no evaluation abnormality signal is generated, and the corresponding evaluation area is marked as a low-interference evaluation area. Although the evaluation results of low-interference evaluation areas are less likely to be disturbed, regular monitoring and tracking are still required.

[0088] In this embodiment, by analyzing the field sampling data and remote sensing data in the heavy metal contaminated soil assessment area, first determine whether the spatial resolution of the two sets of data matches; if not, compare the density of the field sampling points with the pixel resolution of the remote sensing image to evaluate the rationality of the spatial distribution density of the field sampling points, and analyze the impact of different soil types and terrain features on the heavy metal pollution situation to evaluate the accuracy of the data. Comprehensively consider the rationality of the distribution density of the field sampling points and the accuracy of the heavy metal pollution data under different terrain features, determine the consistency of the assessment results, and divide the assessment area into high interference and low interference assessment areas, so that subsequent corresponding treatment measures can be taken for different degrees of interference.

[0089] Example 2

[0090] S6: Further analyze the impact of the consistency of assessment results in the high-interference assessment area, and make early predictions and warnings before risk events occur in heavy metal contaminated soil based on the analysis results.

[0091] When the assessment area is marked as a high-interference assessment area, that is, the interference coefficient of the assessment result consistency of the assessment area within a period of time is greater than or equal to the interference threshold, the interference coefficient of the assessment result consistency of the assessment area in the subsequent period of time is continuously collected, and the collected interference coefficients greater than the interference threshold are used to establish a data set, and the interference coefficients in the data set are compared and analyzed with the interference threshold to calculate the risk coefficient of heavy metal contaminated soil;

[0092] The interference coefficient in the data set is compared with the interference threshold to calculate the risk coefficient of heavy metal contaminated soil. The specific calculation expression is: Where LBF is the risk factor, WPM is the interference threshold, D is the total number of interference coefficients in the set, and WP is r is the interference coefficient greater than the interference threshold in the data set, r is the index of the interference coefficient greater than the interference threshold in the data set, and r=1, 2, 3, ..., z, z is a positive integer greater than 0;

[0093] The obtained ecological risk coefficient is compared with the ecological risk coefficient reference threshold. If the ecological risk coefficient is greater than or equal to the ecological risk coefficient reference threshold, it means that the possibility of ecological risk events occurring in heavy metal contaminated soil is higher, and an early warning signal is generated at this time; early warning measures need to be taken in a timely manner, such as strengthening monitoring, increasing governance efforts, and implementing environmental protection policies, so as to minimize the impact of heavy metal contaminated soil on the environment and human health.

[0094] If the ecological risk coefficient is less than the reference threshold of the ecological risk coefficient, it means that the probability of ecological risk events in heavy metal contaminated soil is lower, and no warning signal is generated at this time. The assessment effect of heavy metal contaminated soil is continuously monitored and evaluated. According to the monitoring results, the early warning model and risk management strategy are adjusted and optimized to ensure timely response and effective management of ecological risks of heavy metal contaminated soil.

[0095] In this embodiment, for the area marked as a high interference assessment area, the interference coefficient of the consistency of the assessment results in the subsequent period of time is collected, and a data set is established. The interference coefficients in the set that are greater than the interference threshold are compared and analyzed to calculate the risk coefficient of heavy metal contaminated soil. According to the comparison between the calculated results and the reference threshold of the ecological risk coefficient, an early warning signal is generated and corresponding measures are taken. The assessment effect is continuously monitored and evaluated, and the early warning model and risk management strategy are adjusted to ensure timely response and effective management of the ecological risks of heavy metal contaminated soil.

[0096] Example 3

[0097] See also Figure 2 As shown, the heavy metal contaminated soil ecological risk assessment based on multi-source analysis described in this embodiment includes a resolution judgment module, a sampling point density analysis module, a pollution data accuracy judgment module, a comprehensive analysis module, an assessment area division module and a prediction and early warning module;

[0098] Resolution judgment module: obtains field sampling data and remote sensing data in the heavy metal contaminated soil assessment area, analyzes the overlapping parts of the two sets of data, and determines whether the spatial resolutions of the two sets of data match;

[0099] Sampling point density analysis module: When the spatial resolutions of the two sets of data do not match, the density of the actual sampling points in the assessment area is obtained, and the density of the actual sampling points is compared with the pixel resolution of the remote sensing image to determine the degree of abnormality of the spatial distribution density of the actual sampling points and evaluate the rationality of the distribution density of the actual sampling points;

[0100] Pollution data accuracy judgment module: Analyze the heavy metal pollution in different soil types in the assessment area, judge the deviation of heavy metal pollution data in different complexities according to the complexity of the terrain characteristics in the assessment area, and evaluate the accuracy of heavy metal pollution data under different terrain characteristics;

[0101] Comprehensive analysis module: Comprehensively analyze the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics to determine the impact of the consistency of the assessment results in the assessment area;

[0102] Assessment area division module: Based on the assessment results, the impact degree of the assessment area is classified and high interference assessment areas and low interference assessment areas are identified;

[0103] Prediction and warning module: further analyze the impact of consistency of assessment results in high-interference assessment areas, and make early predictions and warnings before risk events occur in heavy metal contaminated soil based on the analysis results.

[0104] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0105] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0106] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0107] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0108] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis, characterized by: The following steps are involved: S1: Obtain field sampling data and remote sensing data in the heavy metal contaminated soil assessment area, analyze the overlapping parts of the two sets of data, and determine whether the spatial resolutions of the two sets of data match; S2: When the spatial resolutions of the two sets of data do not match, obtain the density of the field sampling points in the assessment area, compare the density of the field sampling points with the pixel resolution of the remote sensing image, determine the degree of abnormality of the spatial distribution density of the field sampling points, and evaluate the rationality of the distribution density of the field sampling points; S3: Analyze the heavy metal pollution in different soil types in the assessment area, determine the deviation of heavy metal pollution data in different levels of complexity according to the complexity of the terrain characteristics in the assessment area, and evaluate the accuracy of heavy metal pollution data under different terrain characteristics; S4: Comprehensively analyze the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics to determine the impact of the consistency of the assessment results in the assessment area; S5: Based on the assessment results, the impact degree of the assessment area is classified and high interference assessment areas and low interference assessment areas are identified; S6: Further analyze the impact of consistency of assessment results in high-interference assessment areas, and make early predictions and warnings before risk events of heavy metal contaminated soil occur based on the analysis results.

2. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 1, characterized in that: In S1, the spatial coverage overlap of the two sets of data is analyzed to determine whether the spatial resolutions of the two sets of data match, specifically: Count the number of field sampling points and remote sensing pixels in the overlapping area, calculate the average sampling point density and pixel resolution in the overlapping area, and analyze the matching between the two; calculate the average number of sampling points corresponding to each remote sensing pixel: average number of sampling points per pixel = N / M; N is the number of field sampling points, and M is the total pixel area; A comparative analysis is performed on the average number of sampling points per pixel. If the average number of sampling points per pixel is equal to 1, it indicates that the density of field sampling points and the remote sensing pixel resolution are matched; if the average number of sampling points per pixel is greater than 1, it is calibrated as a mismatch; if the average number of sampling points per pixel is less than 1, it is calibrated as a mismatch.

3. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 2 is characterized in that: In S2, the density of field sampling points is compared with the pixel resolution of the remote sensing image to determine the average number of sampling points contained in each remote sensing pixel. The density drift index of the sampling point distribution is obtained according to the uniformity of the spatial distribution of the field sampling points and the remote sensing image to evaluate the rationality of the field sampling point distribution density. The density drift index is obtained as follows: Obtain the field sampling point data and remote sensing data in the assessment area, divide the assessment area into Q pixel units, count the number of field sampling points contained in each pixel, obtain the sampling point density distribution, and mark it as x i ; After summing the density of each sampling point, calculate its ratio to the total number of pixel units, that is, calculate the global average sampling point density, marked as X, and construct the spatial weight matrix W through the adjacency relationship ij , represents the spatial relationship between each pixel unit, i, j are adjacent pixel units, and the density drift index is calculated. The specific calculation expression is: Where LGS is the density drift index, x j For x i Neighboring density distribution of sampling points.

4. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 3 is characterized in that: The obtained density drift index is compared with the standard density value of the spatial distribution of the field sampling points and the remote sensing image. If the density drift index is greater than or equal to the standard density value, an abnormal distribution density signal of the field sampling points is generated; if the density drift index is less than the standard density value, a normal distribution density signal of the field sampling points is generated.

5. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 4 is characterized in that: In S3, the deviation of heavy metal pollution data in different complexity levels is judged, and the pollution migration variation index of heavy metal pollution data is obtained according to the heavy metal migration and accumulation patterns under different terrain categories. The accuracy of heavy metal pollution data under different terrain characteristics is evaluated. The method for obtaining the pollution migration variation index is: Obtain the heavy metal pollution data and terrain feature data in the assessment area, including the spatial location information of different terrain categories; use the kernel density estimation method to estimate the density of heavy metal pollution data under each terrain category, including using the Gaussian kernel function to calculate the kernel density of heavy metal pollution data for each terrain category. The specific calculation expression is: In the formula, K(s) is the kernel density of heavy metal pollution data, s is the spatial position, and the kernel density estimation value of each terrain category is calculated according to the calculated kernel density value of heavy metal pollution data. The specific calculation expression is: Where n is the number of samples, h is the bandwidth parameter, d is the spatial dimension, and s is a is the location of the sample point, a is the sample number, and F(s) is the kernel density estimate for each terrain category; For each terrain category, the semivariogram of heavy metal pollution data is calculated according to the calculated kernel density estimate of each terrain category, and the semivariogram under each terrain category is drawn to observe its changing trend. The change of heavy metal pollution degree between different points is analyzed according to the fitted semivariogram, and the pollution migration variation index is calculated. The specific calculation expression is: Where KG is the pollution migration variation index.

6. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 5, characterized in that: The calculated pollution migration variation index is compared with the heavy metal pollution migration threshold between different points in the historical data. If the pollution migration variation index is greater than or equal to the pollution migration threshold, an abnormal signal of heavy metal pollution data accuracy is generated, and the sampling and evaluation schemes are adjusted, the sampling density in high error areas is increased, and more accurate remote sensing and analysis techniques are used; if the pollution migration variation index is less than the pollution migration threshold, a normal signal of heavy metal pollution data accuracy is generated and no additional processing is required.

7. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 6, characterized in that: In S4, the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics were comprehensively analyzed, specifically: The density drift index and the pollution migration variation index are normalized, and the interference coefficient of the consistency of the assessment results of the assessment area is calculated by the normalized density drift index and the pollution migration variation index.

8. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 7 is characterized in that: In S5, the impact degree of the assessment area is classified to identify the high interference assessment area and the low interference assessment area, specifically: The interference coefficient of the degree of influence of the consistency of the assessment results of the obtained assessment area is compared with the interference threshold. If the interference coefficient of the degree of influence of the consistency of the assessment results of the assessment area is greater than or equal to the interference threshold, an assessment abnormality signal is generated and the corresponding assessment area is marked as a high-interference assessment area; if the interference coefficient of the degree of influence of the consistency of the assessment results of the assessment area is less than the interference threshold, no assessment abnormality signal is generated and the corresponding assessment area is marked as a low-interference assessment area.

9. The method for ecological risk assessment of heavy metal contaminated soil based on multi-source analysis according to claim 8, characterized in that: When the assessment area is marked as a high-interference assessment area, that is, the interference coefficient of the assessment result consistency of the assessment area in a period of time is greater than or equal to the interference threshold, the interference coefficient of the assessment result consistency of the assessment area in the subsequent period of time is continuously collected, and the collected interference coefficients greater than the interference threshold are used to establish a data set, and the interference coefficients in the data set are compared and analyzed with the interference threshold to calculate the risk factor of heavy metal contaminated soil; The obtained ecological risk coefficient is compared with the ecological risk coefficient reference threshold. If the ecological risk coefficient is greater than or equal to the ecological risk coefficient reference threshold, an early warning signal is generated; If the ecological risk coefficient is less than the ecological risk coefficient reference threshold, no warning signal will be generated.

10. A system for assessing ecological risk of heavy metal contaminated soil based on multi-source analysis, used to implement the method for assessing ecological risk of heavy metal contaminated soil based on multi-source analysis according to any one of claims 1 to 9, characterized in that: It includes resolution judgment module, sampling point density analysis module, pollution data accuracy judgment module, comprehensive analysis module, assessment area division module and prediction and early warning module; Resolution judgment module: obtains field sampling data and remote sensing data in the heavy metal contaminated soil assessment area, analyzes the overlapping parts of the two sets of data, and determines whether the spatial resolutions of the two sets of data match; Sampling point density analysis module: When the spatial resolutions of the two sets of data do not match, the density of the actual sampling points in the assessment area is obtained, and the density of the actual sampling points is compared with the pixel resolution of the remote sensing image to determine the degree of abnormality of the spatial distribution density of the actual sampling points and evaluate the rationality of the distribution density of the actual sampling points; Pollution data accuracy judgment module: Analyze the heavy metal pollution in different soil types in the assessment area, judge the deviation of heavy metal pollution data in different complexities according to the complexity of the terrain characteristics in the assessment area, and evaluate the accuracy of heavy metal pollution data under different terrain characteristics; Comprehensive analysis module: Comprehensively analyze the rationality of the distribution density of field sampling points and the accuracy of heavy metal pollution data under different terrain characteristics to determine the impact of the consistency of the assessment results in the assessment area; Assessment area division module: Based on the assessment results, the impact degree of the assessment area is classified and high interference assessment areas and low interference assessment areas are identified; Prediction and warning module: further analyze the impact of consistency of assessment results in high-interference assessment areas, and make early predictions and warnings before risk events occur in heavy metal contaminated soil based on the analysis results.

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