Soil heavy metal multi-dimensional risk coupling evaluation and environmental capacity dynamic prediction method and system

Abstract

The application provides a soil heavy metal multi-dimensional risk coupling evaluation and environmental capacity dynamic prediction method and system, relates to the technical field of data processing, and is based on remote sensing spectral data and environmental physicochemical covariants of a target region to reversely generate heavy metal content data covering the target region; an integrated pollution index is calculated according to the heavy metal content data; probability distributions of exposure parameters of multiple types of people under various exposure approaches are configured, random sampling is performed on the exposure parameters according to the probability distributions, and a health risk probability is determined based on the heavy metal content data and the sampled exposure parameters; a current environmental capacity is calculated according to a risk control benchmark value and the heavy metal content data, and the current environmental capacity is deduced based on a preset heavy metal annual residual rate to obtain an overload risk area; and the application can deduce the change results of the soil heavy metal environmental capacity in multiple future years, and the overload area of the capacity can be delimited and warned in advance.

Description

Technical Field

[0001] This invention relates to data processing technology, and more particularly to a method and system for multidimensional risk coupling assessment and dynamic prediction of environmental capacity of heavy metals in soil. Background Technology

[0002] Heavy metal pollution in soil is characterized by its insidious nature, cumulative effect, irreversibility, and high toxicity. Soils in mining areas and tailings ponds are constantly affected by mining activities, tailings storage, weathering and leaching, and secondary mineral processing, resulting in significant heavy metal contamination. This has become an environmental problem threatening ecological security and human health. Monitoring soil heavy metal content, conducting pollution assessments, assessing human health risks, and predicting environmental capacity are prerequisites for pollution source tracing, risk management, and ecological restoration.

[0003] Currently, monitoring of soil heavy metal environmental capacity mainly employs the following method: based on the concentration obtained from discrete points using manual grid sampling, combined with regional soil environmental quality standards, the remaining carrying capacity of the soil for heavy metals at the current moment is calculated. This method yields a static capacity at a fixed point in time, lacking analysis of the time dimension. Heavy metals in soil are subject to the combined effects of continuous pollution input, leaching output, and natural decay, causing their accumulation in the soil to change year by year, and consequently, the soil's carrying capacity to change accordingly. Existing technologies cannot extrapolate this year-on-year capacity change, thus making it impossible to predict the extent to which the soil carrying capacity of a certain area will decline in the coming years, nor to predict when the carrying capacity will be exhausted. This forces operation and maintenance management to react passively after the capacity is depleted, preventing advance planning.

[0004] Therefore, how to predict the changes in soil heavy metal environmental capacity over multiple future years and to delineate and warn of areas with overcapacity in advance has become a critical issue that urgently needs to be addressed. Summary of the Invention

[0005] This invention provides a method and system for multidimensional risk coupling assessment and dynamic prediction of environmental capacity of heavy metals in soil. It can predict the changes in environmental capacity of heavy metals in soil over multiple future years and delineate and warn of areas with capacity overload in advance.

[0006] A first aspect of the present invention provides a method for multidimensional risk coupling assessment and dynamic prediction of environmental capacity of heavy metals in soil, comprising:

[0007] Based on remote sensing spectral data and environmental physicochemical covariates of the target area, heavy metal content data covering the target area are generated by inversion.

[0008] The integrated pollution index was calculated based on the heavy metal content data.

[0009] A probability distribution is configured for the exposure parameters of various population groups under different exposure routes. Random sampling is performed on each exposure parameter according to the probability distribution. The probability of health risk is determined based on the heavy metal content data and the sampled exposure parameters.

[0010] The current environmental capacity is calculated based on the risk control benchmark value and the heavy metal content data. The current environmental capacity is extrapolated based on the preset annual heavy metal residue rate to obtain the overload risk zone.

[0011] Alternatively, in one possible implementation of the first aspect,

[0012] Before generating heavy metal content data covering the target area based on remote sensing spectral data and environmental physicochemical covariates of the target area, the method further includes:

[0013] The original hyperspectral image of the target area was acquired as remote sensing spectral data. Pre-set sampling points in the target area were collected, and the collected soil moisture content, pH value and organic matter content were used as environmental physicochemical covariates.

[0014] Radiometric calibration and atmospheric correction are performed on the remote sensing spectral data to obtain surface reflectance images;

[0015] The soil abundance of each pixel in the surface reflectance image is determined, and pixels with soil abundance greater than or equal to a preset abundance threshold are extracted as pure soil pixels. The spectral data of the pure soil pixels are extracted, and a spectral feature set is generated based on the spectral data of the pure soil pixels.

[0016] Optionally, in one possible implementation of the first aspect, the inversion to generate heavy metal content data covering the target region includes:

[0017] The spectral feature set is filtered to obtain the characteristic bands that are sensitive to the target heavy metal;

[0018] The characteristic bands and environmental physicochemical covariates are matrix-concatenated and input into a pre-trained machine learning regression model for inversion, to obtain the heavy metal inversion concentration of each pure soil pixel;

[0019] The target area is divided into multiple computing units according to a preset spatial grid. The center coordinates of each computing unit and the corresponding heavy metal inversion concentration are extracted to obtain heavy metal content data.

[0020] Optionally, in one possible implementation of the first aspect, it also includes:

[0021] Determine whether there are any missing heavy metal inversion concentration calculation units in the heavy metal content data;

[0022] When there are missing computational units for heavy metal inversion concentration, a spatial interpolation algorithm is invoked to complete the heavy metal inversion concentration for the corresponding computational unit.

[0023] Optionally, in one possible implementation of the first aspect, calculating the integrated pollution index based on the heavy metal content data includes:

[0024] Based on the heavy metal content data, single-element evaluation indicators with multiple evaluation dimensions are calculated, including geoaccumulation index, enrichment factor, pollution factor and ecological risk index.

[0025] The integrated pollution index is obtained by normalizing each individual element evaluation index and then summing them by weight.

[0026] Optionally, in one possible implementation of the first aspect, the step of normalizing each individual element evaluation index and then performing a weighted sum to obtain the integrated pollution index includes:

[0027] Based on the grading standards of each individual evaluation indicator, determine the risk level and maximum number of levels corresponding to each individual evaluation indicator;

[0028] The risk level corresponding to each individual evaluation indicator is divided by the maximum level number and then mapped to a percentage scale to obtain the risk score of each individual evaluation indicator.

[0029] The weight value corresponding to each single-element evaluation index is determined based on the degree of dispersion of each of the aforementioned hazard scores;

[0030] The integrated pollution index is obtained by multiplying each hazard score by its corresponding weight value and then summing the results.

[0031] Optionally, in one possible implementation of the first aspect, determining the weight value corresponding to each single-element evaluation index based on the dispersion of each of the danger scores includes:

[0032] The hazard scores corresponding to each individual evaluation index are normalized to obtain standardized hazard scores.

[0033] Based on the standardized hazard score, the information entropy of each individual evaluation index is calculated using the entropy weight method.

[0034] The weight values ​​are determined based on the information entropy of each individual evaluation index.

[0035] Optionally, in one possible implementation of the first aspect, the step of randomly sampling each exposure parameter according to the probability distribution and determining the health risk probability based on heavy metal content data and the sampled exposure parameters includes:

[0036] The exposure routes include oral exposure, skin exposure, and respiratory exposure.

[0037] Based on heavy metal content data and exposure parameters obtained from sampling, the average non-carcinogenic and average carcinogenic daily exposures of various population groups under oral, skin, and respiratory exposure routes were calculated.

[0038] The non-carcinogenic average daily exposure to each exposure route is divided by the corresponding reference dose and then summed to obtain the non-carcinogenic risk index.

[0039] The carcinogenic risk index is obtained by multiplying the average daily carcinogenic exposure amount under each exposure route by the corresponding carcinogenic slope factor and then summing them up.

[0040] The non-carcinogenic risk index and the carcinogenic risk index are compared with their respective preset safety thresholds to determine the probability of health risk.

[0041] Optionally, in one possible implementation of the first aspect, the process of extrapolating the current environmental capacity based on a preset annual heavy metal residue rate to obtain an overload risk zone includes:

[0042] Based on the preset annual heavy metal residue rate, risk control benchmark value and heavy metal content data, the predicted environmental capacity of each calculation unit under multiple prediction years is calculated.

[0043] Calculation units with current or predicted environmental capacity less than or equal to 0 are selected as overload units, and the area formed by the overload units is obtained as the overload risk zone.

[0044] A second aspect of the present invention provides a multidimensional risk coupling assessment and dynamic prediction system for soil heavy metals, comprising:

[0045] The inversion module is used to generate heavy metal content data covering the target area based on remote sensing spectral data and environmental physicochemical covariates of the target area.

[0046] The calculation module is used to calculate the integrated pollution index based on the heavy metal content data;

[0047] The sampling module is used to configure probability distributions for exposure parameters of multiple population groups under various exposure routes, randomly sample each exposure parameter according to the probability distribution, and determine the probability of health risk based on heavy metal content data and the sampled exposure parameters.

[0048] The extrapolation module is used to calculate the current environmental capacity based on the risk control benchmark value and the heavy metal content data, and extrapolate the current environmental capacity based on the preset annual heavy metal residue rate to obtain the overload risk zone.

[0049] The beneficial effects of this invention are as follows:

[0050] 1. This invention calculates the current environmental capacity based on risk control benchmark values ​​and heavy metal content data, and extrapolates the current environmental capacity based on a preset annual heavy metal residue rate. This yields the predicted environmental capacity for each calculation unit over multiple forecast years. Calculation units with a current or predicted environmental capacity less than or equal to 0 are designated as overload units, forming overload risk zones. This invention enables the prediction of environmental capacity over multiple future years, solving the problem that existing technologies can only determine static capacity at a fixed moment and cannot predict the annual changes in capacity. This invention can delineate and warn of overload risk zones before capacity is exhausted.

[0051] 2. This invention calculates single-element evaluation indicators based on heavy metal content data, considering multiple evaluation dimensions such as geoaccumulation index, enrichment factor, pollution factor, and ecological risk index. After normalizing each single-element evaluation indicator, a weighted sum is performed to obtain an integrated pollution index. This invention normalizes multiple single-element evaluation indicators with different classification standards and dimensions to a unified scale before weighted summation, solving the problems of one-sided evaluation by a single indicator and the inability to directly integrate multiple indicators due to inconsistent classification standards and dimensions, thus obtaining an integrated pollution index that comprehensively reflects multiple evaluation dimensions.

[0052] 3. This invention addresses the diverse physiological characteristics of different population groups by configuring probability distributions for exposure parameters under various exposure pathways. Through multiple random sampling of exposure parameters, multiple sets of non-carcinogenic risk indices and carcinogenic risk indices are obtained. Each set of non-carcinogenic risk indices and carcinogenic risk indices is then compared with its corresponding preset safety threshold to calculate the probability of exceeding the threshold, thus obtaining the health risk probability differentiated by population group. This invention solves the problem of existing technologies using fixed exposure parameters to calculate a single risk value, ignoring the differences in exposure parameters between different population groups, and obtains a health risk probability differentiated by population group that reflects the likelihood of exceeding the health risk limit.

[0053] 4. This invention assesses the target area based on heavy metal content data across the entire region, considering three dimensions: pollution level, health risk, and capacity evolution. The integrated pollution index reflects the current overall pollution level of each calculation unit, the health risk probability reflects the potential health hazards of heavy metals to various population groups, and the overload risk zone reflects the capacity evolution of each calculation unit over the predicted future years. Furthermore, by integrating the pollution index, health risk probability, and overload risk zone, a comprehensive display of the pollution status, health risks, and capacity evolution of the target area can be provided, offering reference information for risk management. Attached Figure Description

[0054] Figure 1A flowchart of a method for multidimensional risk coupling assessment and dynamic prediction of environmental capacity of heavy metals in soil provided by the present invention;

[0055] Figure 2 This is a schematic diagram of the structure of a multidimensional risk coupling assessment and dynamic prediction system for heavy metals in soil and environmental capacity provided by the present invention.

[0056] Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided by the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0059] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply 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 invention.

[0060] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0061] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0062] It should be understood that in this invention, "B corresponding to A", "B corresponding to A", "A and B correspond", or "B and A correspond" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information. Matching A and B is defined as a similarity between A and B that is greater than or equal to a preset threshold.

[0063] Depending on the context, "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection."

[0064] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0065] This invention provides a method for multidimensional risk coupling assessment and dynamic prediction of environmental capacity for heavy metals in soil. (See also...) Figure 1 ,include:

[0066] S1, Based on the remote sensing spectral data and environmental physicochemical covariates of the target area, the heavy metal content data covering the target area is generated by inversion.

[0067] The target area refers to the contaminated soil area that requires heavy metal pollution assessment and environmental capacity prediction, such as mining areas, tailings pond areas, or farmland in wastewater irrigation areas; remote sensing spectral data refers to the spectral image data obtained by imaging the target area through a remote sensing platform; environmental physicochemical covariates refer to the soil physicochemical indicators measured after collecting soil samples in the target area.

[0068] It is important to note that to determine the heavy metal pollution status of soil in a mining area, it is first necessary to determine the exact amount of heavy metals in the soil. Retrieval solely through remote sensing spectroscopy is insufficiently accurate because soil pH, organic matter, and moisture content all affect spectral representation. The same heavy metal content may exhibit different spectra in soils under different conditions. This step simultaneously acquires data from both remote sensing spectral data and field-collected environmental physicochemical covariates—two different sources—to provide spectral information for the retrieval and to correct for soil background differences using these environmental physicochemical covariates.

[0069] Understandably, remote sensing spectral data is acquired by imaging the target area through a remote sensing platform, while environmental physicochemical covariates are obtained by collecting soil samples in the target area and measuring their physicochemical indicators. The remote sensing spectral data and environmental physicochemical covariates are used together as inputs for retrieving heavy metal content data.

[0070] In some embodiments, steps S1-S03 are included before step S1:

[0071] S01, acquire the original hyperspectral image of the target area as remote sensing spectral data, collect data at preset sampling points in the target area, and use the collected soil moisture content, pH value and organic matter content as environmental physicochemical covariates.

[0072] Understandably, a drone carrying a hyperspectral camera is used to fly over the target area and collect raw hyperspectral images. Before the drone takes off, the hyperspectral camera undergoes dark current correction and standard whiteboard calibration. Dark current correction is used to eliminate the camera's own thermal noise, and standard whiteboard calibration uses a standard reflectance whiteboard to obtain a reference spectrum. The geographic coordinates of ground control points are obtained through real-time kinematics in conjunction with the Global Navigation Satellite System. This positioning data is then used for geometric fine correction of the hyperspectral images to ensure that the hyperspectral pixels correspond one-to-one with the ground-based measured test points in spatial location. During on-site sampling, sampling points were arranged in a regular grid according to the target area. Sampling points were densely deployed in high-risk pollution areas, taking into account the distribution of pollution sources, land use types, and terrain features. Topsoil samples from 0 to 20 cm were collected. After being naturally air-dried indoors, ground, and passed through a 100-mesh nylon sieve, the samples were digested with a mixed acid of HNO3-HF-HClO4. The measured heavy metal content was determined using inductively coupled plasma atomic emission spectrometry, atomic fluorescence spectrometry, and inductively coupled plasma mass spectrometry. Soil pH was determined using the potentiometry method, organic matter content was determined using the potassium dichromate titration method, and moisture content was determined using the drying method.

[0073] Among them, the original hyperspectral image refers to the uncorrected spectral image directly output by the hyperspectral camera carried by the UAV to the target area; the sampling point refers to the location point where soil samples are collected on-site in the target area; the soil moisture content refers to the proportion of water contained in a unit mass of soil; the pH value refers to the acidity or alkalinity of the soil; and the organic matter content refers to the proportion of organic matter contained in a unit mass of soil.

[0074] S02, Radiometric calibration and atmospheric correction are performed on the remote sensing spectral data to obtain a surface reflectance image.

[0075] Understandably, the original hyperspectral imagery acquired in step S01 is retrieved, and its digital quantization values ​​are converted into radiance values ​​based on preset calibration parameters. Then, the FLAASH atmospheric correction algorithm based on the MODTRAN radiative transfer model is used to restore the true surface reflectance. Orthorectification and geometric registration are performed using ground control points acquired on-site. Seamless mosaicking and resampling optimization of multiple images are achieved through image processing software. Finally, a Bayesian super-resolution algorithm is used to enhance the spatial accuracy of the original hyperspectral imagery, resulting in a surface reflectance image.

[0076] S03, determine the soil abundance of each pixel in the surface reflectance image, extract pixels with soil abundance greater than or equal to a preset abundance threshold as pure soil pixels, extract the spectral data of the pure soil pixels, and generate a spectral feature set based on the spectral data of the pure soil pixels.

[0077] Understandably, based on field-collected data, an endmember spectral library covering soil, water, vegetation, and buildings is constructed. A fully constrained least squares algorithm is then used to unmix the surface reflectance image at the pixel level, yielding an abundance probability map of soil components in each pixel. Preferably, an abundance threshold of 0.6 is set. Pixels with a soil abundance greater than or equal to 0.6 are classified as pure soil pixels and retained, while pixels with a soil abundance less than 0.6 are excluded. Excluded pixels include both mixed pixels (soil mixed with other land features) and pixels containing non-soil land features such as water, vegetation, or buildings. The spectral data of the retained pixels are then extracted.

[0078] Furthermore, the Savitzky-Golay convolutional smoothing filter is invoked to perform polynomial fitting on the spectral data within the moving window to suppress high-frequency noise; then, a standard normal transformation is applied to eliminate the scattering effects and baseline drift caused by soil particle size inhomogeneity and surface roughness; finally, a first-order differential transformation is performed to reduce the influence of background absorption and baseline shift, highlighting the weak absorption characteristics of heavy metals, and the spectral data processed in the above way are collected into a spectral feature set.

[0079] Among them, the pixel unmixing algorithm refers to the algorithm in the existing technology that decomposes the spectrum of a pixel into the spectra of multiple land cover endmembers and their abundance; a pure soil pixel refers to a pixel whose soil abundance is greater than or equal to the abundance threshold; the abundance threshold refers to the critical value of soil abundance used to determine whether a pixel is a pure soil pixel; the endmember spectral library refers to a reference set composed of pure spectra of various typical land cover types such as soil, water, vegetation and buildings; abundance refers to the proportion of a certain type of land cover in a pixel.

[0080] In some embodiments, step S1 includes S11-S13:

[0081] It should be noted that in this embodiment, the inversion relationship between spectral features and measured heavy metal content is first established, and then the inversion relationship is applied to each pure soil pixel to obtain the heavy metal inversion concentration of each pure soil pixel. Spatial interpolation is then performed on the concentration at the location of the excluded pixel to obtain spatially continuous heavy metal content data.

[0082] Among them, heavy metal content data refers to a spatially continuous dataset composed of the location coordinates of each computing unit and its corresponding heavy metal inversion concentration.

[0083] S11, the spectral feature set is screened to obtain the feature bands that are sensitive to the target heavy metal.

[0084] Among them, target heavy metals refer to heavy metal elements in the target area that need to be evaluated and predicted; characteristic bands refer to spectral bands that are sensitive to target heavy metals and are selected from the set of spectral features.

[0085] Understandably, this involves retrieving a spectral feature set, using the measured heavy metal content as the dependent variable, and employing a competitive adaptive reweighted sampling algorithm to gradually eliminate wavelength variables that contribute little to the model, retaining bands sensitive to heavy metal content as feature bands. For example, 30 to 40 feature bands sensitive to the target heavy metal are selected from 340 spectral channels.

[0086] S12, the characteristic bands and environmental physicochemical covariates are matrix-concatenated and input into a pre-trained machine learning regression model for inversion, to obtain the heavy metal inversion concentration of each pure soil pixel.

[0087] Understandably, the measured heavy metal content at each sampling point is matched with the characteristic bands and environmental physicochemical covariates of that point. The Kennard-Stone algorithm is then used to divide the samples into training and validation sets at a 7:3 ratio. A machine learning regression model is constructed using the characteristic bands and environmental physicochemical covariates as input variables and the measured heavy metal content as the output variable. The measured heavy metal content in the validation set is compared one by one with the model's predicted content. A qualified model is deemed to have a determination coefficient of not less than 0.8, a root mean square error not exceeding 10% of the mean measured content, and a relative analysis error of not less than 2.5 on the validation set. The qualified model is then applied to the feature matrix of each pure soil pixel to obtain the heavy metal inversion concentration for each pure soil pixel. Preferably, the machine learning regression model uses partial least squares regression, support vector regression, backpropagation neural network, XGBoost, or random forest models.

[0088] Among them, the machine learning regression model refers to a regression model that takes feature bands and environmental physicochemical covariates as inputs and heavy metal content as output. This model is trained using the measured heavy metal content of the sampling points as training labels. The heavy metal inversion concentration refers to the predicted value of heavy metal content at the pixel output by the machine learning regression model after inputting the feature bands and environmental physicochemical covariates of the pure soil pixel into the model.

[0089] S13, the target area is divided into multiple computing units according to the preset spatial grid, and the center coordinates and corresponding heavy metal inversion concentrations of each computing unit are extracted to obtain heavy metal content data.

[0090] Understandably, the process involves acquiring a georegistered hyperspectral image coordinate system and dividing the target area into units based on a preset spatial grid, such as a 0.2-meter by 0.2-meter grid, to obtain multiple computational units. The center coordinates of each computational unit and the corresponding heavy metal inversion concentration are then extracted, and the center coordinates of all computational units and the heavy metal inversion concentration are combined to obtain heavy metal content data.

[0091] Here, a computational unit refers to a single spatial grid obtained after dividing the target area according to a preset spatial grid; the center coordinates refer to the coordinates corresponding to the geometric center of the computational unit; and the preset spatial grid refers to a grid with pre-set dimensions when dividing the target area into computational units.

[0092] In some embodiments, it also includes:

[0093] S14, determine whether there is a missing heavy metal inversion concentration calculation unit in the heavy metal content data.

[0094] It is understandable that the calculation units in the heavy metal content data are traversed to determine whether there are any calculation units that do not have corresponding heavy metal inversion concentrations due to image edges or ground cover obstruction.

[0095] S15, when there are missing calculation units for heavy metal inversion concentration, call the spatial interpolation algorithm to complete the heavy metal inversion concentration of the corresponding calculation unit.

[0096] Understandably, when there are missing computational units with inverted heavy metal concentrations, the Kriging spatial interpolation algorithm or the inverse distance weighting algorithm is called to estimate and complete the missing unit by using the inverted heavy metal concentrations of the computational units surrounding the missing unit, thus obtaining spatially continuous heavy metal content data.

[0097] Spatial interpolation algorithms refer to existing algorithms that estimate values ​​at unknown locations using values ​​at known locations.

[0098] S2, the integrated pollution index is calculated based on the heavy metal content data.

[0099] It should be noted that when evaluating the degree of heavy metal pollution in soil, the geoaccumulation index, enrichment factor, pollution factor, and ecological risk index each reflect the pollution level from different perspectives. The geoaccumulation index refers to the degree of accumulation of anthropogenic pollution, the enrichment factor is used to distinguish the causes of heavy metal enrichment, the pollution factor reflects the multiple of the element exceeding the background value, and the ecological risk index highlights the ecological risks caused by the biotoxicity of heavy metals. Using only one of these indicators can only reflect one aspect of the pollution, and the evaluation results are prone to being one-sided. Furthermore, the number of levels for these four indicators is not consistent: the geoaccumulation index is divided into 7 levels, the enrichment factor and ecological risk index into 5 levels, and the pollution factor into 4 levels. Moreover, their dimensional systems are also different. If their values ​​are directly weighted and added together, the indicator with more levels or larger values ​​will dominate the results, causing the evaluation to be distorted.

[0100] Understandably, for each calculation unit in the heavy metal content data, multiple evaluation dimensions of single-element evaluation indicators are calculated one by one. Each single-element evaluation indicator is mapped to a unified percentage scale to obtain a corresponding hazard score. The weight value corresponding to each single-element evaluation indicator is determined based on the dispersion of each hazard score. Each hazard score is multiplied by its corresponding weight value and then summed to obtain an integrated pollution index. The pollution level is then determined based on the numerical range of the integrated pollution index. Preferably, pollution is classified based on the percentage-based integrated pollution index: an integrated pollution index less than 20 is considered safe; an integrated pollution index greater than or equal to 20 and less than 40 is considered slightly polluted; an integrated pollution index greater than or equal to 40 and less than 60 is considered moderately polluted; an integrated pollution index greater than or equal to 60 and less than 80 is considered heavily polluted; and an integrated pollution index greater than or equal to 80 is considered severely polluted.

[0101] Among them, the single-element evaluation index refers to the index that reflects the degree of heavy metal pollution in soil from a certain evaluation dimension; the evaluation dimension refers to the measurement angle that reflects the degree of heavy metal pollution in soil; the percentage scale refers to the standard scale for uniform mapping of various single-element evaluation indices with different levels and dimensions, with a value range of 0 to 100; the hazard score refers to the score obtained after mapping the single-element evaluation index to the percentage scale; the integrated pollution index refers to the comprehensive pollution index obtained by multiplying each hazard score by its corresponding weight value and then summing them up; and the pollution level refers to the pollution level determined based on the numerical range of the integrated pollution index.

[0102] In some embodiments, step S2 includes S21-S22:

[0103] S21. Based on the heavy metal content data, calculate single-element evaluation indicators with multiple evaluation dimensions, including geoaccumulation index, enrichment factor, pollution factor and ecological risk index.

[0104] It is understandable that the cumulative index, enrichment factor, pollution factor, and ecological risk index are calculated one by one for each calculation unit in the heavy metal content data.

[0105] The geocumulative index is obtained using the following formula. ,

[0106]

[0107] in, The heavy metal content of a pixel; For regional background values, such as those of the heavy metal element arsenic (As). The value can be 9.44 mg / kg; the 1.5 in the formula is a correction factor to eliminate the influence of geological background fluctuations. Pollution is classified into 7 levels based on the geoaccumulation index. When less than or equal to 0, it is judged as level 1; when A value greater than 0 and less than or equal to 1 is classified as Level 2. A value greater than 1 and less than or equal to 2 is classified as level 3. A value greater than 2 and less than or equal to 3 is classified as level 4. A value greater than 3 and less than or equal to 4 is classified as level 5. A value greater than 4 and less than or equal to 5 is classified as level 6. A value greater than 5 is classified as level 7.

[0108] The enrichment factor is obtained using the following formula. ,

[0109]

[0110] in, For the current computing unit content, for Reference values ​​for elements. Based on enrichment factors, contamination is classified into 5 levels. When it is less than 2, it is judged as level 1. A value greater than or equal to 2 and less than 5 is classified as Level 2. A value greater than or equal to 5 and less than 20 is classified as Level 3. A score greater than or equal to 20 and less than 40 is classified as Level 4. A score of 40 or higher is classified as Level 5.

[0111] Pollutant factors are obtained using the following formula. ,

[0112]

[0113] Pollution is classified into four levels based on pollutants. When it is less than 1, it is judged as level 1. A value greater than or equal to 1 and less than 3 is classified as Level 2. A value greater than or equal to 3 and less than 6 is classified as level 3. A value greater than or equal to 6 is classified as Level 4.

[0114] The ecological risk index is obtained using the following formula. ,

[0115]

[0116] in, This represents the toxicity coefficient. Based on the ecological risk index, pollution is divided into five levels. When it is less than 40, it is judged as Level 1. A score greater than or equal to 40 and less than 80 is classified as Level 2. A score greater than or equal to 80 and less than 160 is classified as Level 3. A value greater than or equal to 160 and less than 320 is classified as Level 4. A value greater than or equal to 320 is classified as Level 5.

[0117] In addition, the geoaccumulation index is a single-element evaluation index that reflects the degree of accumulation of heavy metals by human pollution; the enrichment factor is a single-element evaluation index used to distinguish the causes of heavy metal enrichment; the pollution factor is a single-element evaluation index that reflects the multiple by which heavy metals exceed the background value; the ecological risk index is a single-element evaluation index that reflects the degree of ecological risk caused by the biotoxicity of heavy metals; the background value is the inherent content of heavy metal elements in the target area when it is not polluted by human activities; and the toxicity coefficient is a coefficient that reflects the strength of the biotoxicity of heavy metal elements.

[0118] S22, after normalizing the evaluation indicators of each individual element, perform a weighted summation to obtain the integrated pollution index.

[0119] In some embodiments, step S22 (normalizing each individual element evaluation index and then performing a weighted summation to obtain the integrated pollution index) includes S221-S224:

[0120] S221. Based on the respective grading standards of each individual evaluation indicator, determine the risk level and maximum number of levels corresponding to each individual evaluation indicator.

[0121] Understandably, for the current calculation unit, based on the respective grading standards of the geoaccumulation index, enrichment factor, pollution factor, and ecological risk index, the current risk level of each single-element evaluation indicator is determined, and the maximum number of levels in the grading standards of each single-element evaluation indicator is determined. The maximum number of levels for the geoaccumulation index is 7, the maximum number of levels for the enrichment factor is 5, the maximum number of levels for the pollution factor is 4, and the maximum number of levels for the ecological risk index is 5.

[0122] Among them, risk level refers to the level of a single-element evaluation indicator according to its grading standard; maximum level number refers to the total number of levels in the grading standard of a single-element evaluation indicator.

[0123] S222: Divide the risk level corresponding to each individual evaluation indicator by the maximum level number and map it onto a percentage scale to obtain the danger score of each individual evaluation indicator.

[0124] Understandably, the risk level corresponding to each individual evaluation indicator is divided by the maximum number of levels for that indicator, and then multiplied by 100 and mapped onto a percentage scale to obtain the hazard score corresponding to that individual evaluation indicator.

[0125] The hazard score corresponding to the geocumulative index is obtained using the following formula. ,

[0126]

[0127] The hazard score corresponding to the enrichment factor is obtained using the following formula. ,

[0128]

[0129] The hazard score corresponding to the pollution factor is obtained using the following formula. ,

[0130]

[0131] The hazard score corresponding to the ecological risk index is obtained using the following formula. ,

[0132]

[0133] in, These are the risk levels corresponding to the geoaccumulation index, enrichment factor, pollution factor, and ecological risk index, respectively.

[0134] S223, determine the weight value corresponding to each single-element evaluation index based on the dispersion of each of the aforementioned hazard scores.

[0135] In some embodiments, step S223 (determining the weight value corresponding to each single-element evaluation index based on the dispersion of each of the danger scores) includes S2231-S2233:

[0136] S2231, normalize the hazard scores corresponding to each single-element evaluation index to obtain standardized hazard scores.

[0137] Understandably, for all n computational units in the domain, for each single-element evaluation index, the extreme value normalization method is used to process its corresponding hazard score to obtain a standardized hazard score. Specifically, the standardized hazard score can be obtained through the following formula.

[0138]

[0139] in, No. The single-element evaluation index in the first Standardized hazard scores on each computational unit For the first The single-element evaluation index in the first The danger score on each calculation unit For the first The minimum risk score of each single-element evaluation index across all computational units in the entire domain. For the first The maximum value of the hazard score for each single-element evaluation index across all computational units in the entire domain.

[0140] It should be noted that when and When they are equal, it indicates that the risk scores of the single-element evaluation index are the same in all calculation units of the entire domain. In this case, the standardized risk score corresponding to the single-element evaluation index is set to 0.

[0141] Furthermore, for each individual evaluation indicator, its weight in the risk score of each calculation unit is calculated. The formula for calculating the weight is as follows:

[0142]

[0143] in, For the first The single-element evaluation index in the first The proportion of the danger score on each calculation unit, This represents the number of calculation units.

[0144] S2232, based on the standardized hazard score, the information entropy of each individual evaluation index is calculated using the entropy weight method.

[0145] Understandably, for each individual evaluation indicator, the information entropy of that indicator is calculated based on the weight of the standardized hazard score. The formula for the information entropy of a single-element evaluation index is as follows:

[0146]

[0147] in, For the first Information entropy of a single-element evaluation index.

[0148] In addition, information entropy refers to the numerical value that reflects the degree of dispersion of the risk score corresponding to a single-element evaluation index. The greater the degree of dispersion of the risk score corresponding to a single-element evaluation index, the smaller the corresponding information entropy.

[0149] S2233, determine the weight value based on the information entropy of each single-element evaluation index.

[0150] It is understandable that, for each single-element evaluation indicator, the corresponding weight value is calculated based on the information entropy of that single-element evaluation indicator. The formulas for the weight values ​​corresponding to each single-element evaluation index are as follows:

[0151]

[0152] in, For the first The weight value corresponding to each single-element evaluation index.

[0153] S224. The integrated pollution index is obtained by multiplying each hazard score by its corresponding weight value and then summing the results.

[0154] Understandably, after obtaining each hazard score and its corresponding weight, each hazard score is multiplied by its corresponding weight, and then the results are summed to obtain the integrated pollution index. The integrated pollution index is obtained using the following formula:

[0155]

[0156] in, To integrate pollution indices, These are the weight values ​​corresponding to the geoaccumulation index, enrichment factor, pollution factor, and ecological risk index, respectively.

[0157] S3, configure probability distributions for exposure parameters of multiple population groups under various exposure routes, randomly sample each exposure parameter according to the probability distribution, and determine the health risk probability based on heavy metal content data and the sampled exposure parameters.

[0158] It should be noted that when assessing the health risks of heavy metals, exposure parameters such as soil ingestion rate, body weight, and exposure frequency are not constant in the population; there are natural differences between individuals. Furthermore, adult men, adult women, and children differ significantly in physiological and behavioral characteristics, with children typically having higher intake per unit body weight. Therefore, for exposure parameters that differ between non-carcinogenic and carcinogenic risk assessments, corresponding probability distributions are configured for each assessment.

[0159] Understandably, for various population groups with different physiological characteristics, probability distributions are configured for exposure parameters under different exposure routes for each group. For each calculation unit in the heavy metal content data, multiple random sampling calculations are performed on the exposure parameters according to the probability distribution. The exposure parameter values ​​obtained from each sampling are substituted into the average daily exposure calculation formula along with the heavy metal content data of the calculation unit. Each sampling yields a set of non-carcinogenic risk indices and carcinogenic risk indices. Multiple samplings yield multiple sets of non-carcinogenic risk indices and carcinogenic risk indices. Each set of non-carcinogenic risk indices and carcinogenic risk indices is compared with the corresponding preset safety thresholds. The proportion of times the corresponding preset safety threshold is exceeded to the total number of samplings is used as the exceedance probability. The exceedance probabilities of each calculation unit for each population group are arranged across the entire target area based on the center coordinates of the calculation unit to obtain the health risk probabilities differentiated by population group. Preferably, the population is divided into three categories: adult males, adult females, and children. For each calculation unit, at least 2000 random sampling iterations are performed independently for each population group.

[0160] Among them, the exposure route refers to the way heavy metals enter the human body; the exposure parameter refers to the parameter used to calculate the exposure amount of the population, which represents the average daily intake of heavy metals by the population under a certain exposure route; the probability distribution refers to the value distribution of the exposure parameter in the population; the preset safety threshold refers to the pre-set threshold used to determine whether the non-carcinogenic risk index or the carcinogenic risk index exceeds the standard; and the health risk probability refers to the set of exceedance probabilities obtained by arranging the exceedance probabilities of each calculation unit for various population groups on the target area based on the center coordinates of the calculation unit.

[0161] It is worth noting that for exposure parameters that differ between the non-carcinogenic and carcinogenic risk assessment models, corresponding probability distributions should be configured separately. Exposure parameters such as mean time (AT) and adsorption factor (ABS) take different values ​​in the non-carcinogenic and carcinogenic risk assessments, requiring independent configuration of probability distributions for each.

[0162] In some embodiments, step S3 (randomly sampling each exposure parameter according to the probability distribution, and determining the health risk probability based on heavy metal content data and the sampled exposure parameters) includes S31-S35:

[0163] S31, the exposure routes include oral exposure, skin exposure and respiratory exposure.

[0164] It is understood that in this invention, oral exposure refers to exposure via oral ingestion, skin exposure refers to exposure via skin contact, and respiratory exposure refers to exposure via inhalation.

[0165] S32, based on heavy metal content data and exposure parameters obtained from sampling, calculates the average daily non-carcinogenic and average daily carcinogenic exposure for various population groups under oral, skin, and respiratory exposure routes.

[0166] Among them, the non-carcinogenic average daily exposure refers to the average daily exposure calculated after sampling based on the probability distribution of exposure parameters corresponding to the non-carcinogenic risk assessment; the carcinogenic average daily exposure refers to the average daily exposure calculated after sampling based on the probability distribution of exposure parameters corresponding to the carcinogenic risk assessment; the average daily exposure refers to the average amount of heavy metals ingested by a population under a certain exposure route.

[0167] Understandably, for non-carcinogenic risk assessment, random sampling is performed on each exposure parameter according to the probability distribution corresponding to the non-carcinogenic risk assessment to obtain a set of non-carcinogenic exposure parameter values. These non-carcinogenic exposure parameter values, along with the heavy metal content data of the current calculation unit, are substituted into the average daily exposure calculation formula to obtain the non-carcinogenic average daily exposure for each exposure route under this sampling. Similarly, for carcinogenic risk assessment, random sampling is performed on each exposure parameter according to the probability distribution corresponding to the carcinogenic risk assessment to obtain a set of carcinogenic exposure parameter values. These carcinogenic exposure parameter values, along with the heavy metal content data of the current calculation unit, are substituted into the average daily exposure calculation formula to obtain the carcinogenic average daily exposure for each exposure route under this sampling. Multiple samplings yield multiple sets of non-carcinogenic average daily exposure and carcinogenic average daily exposure.

[0168] The average daily exposure via the oral route is obtained using the following formula. ,

[0169]

[0170] The average daily exposure via skin exposure routes can be obtained using the following formula. ,

[0171]

[0172] The average daily exposure to respiratory exposure routes can be obtained using the following formula. ,

[0173]

[0174] in, For soil uptake rate, For exposure frequency, For exposure years, For weight, For average time, Area of ​​skin contact. As a skin adhesion factor, As an adsorption factor, Respiratory rate, This refers to the particulate emission factor.

[0175] Understandably, the population is divided into adult men, adult women, and children, and probability distributions are configured for exposure parameters of each group. Preferably, soil ingestion rate is configured as a triangular distribution: Tri(4, 30, 52) for adult men and women, and Tri(66, 103, 161) for children; respiratory rate is configured as a normal distribution: Nor(15.94, 3.12) for adult men and women, and Nor(11.68, 2.17) for children; exposure frequency is configured as a triangular distribution: Tri(180, 345, 365) for the entire population; exposure years are configured as a uniform distribution: Uni(0, 50) for adult men and women, and Uni(0, 18) for children; and weight is configured as a triangular distribution: Tri(43.6, 66.15, 161) for adult men. The values ​​for adult females were Tri(36.6, 57.82, 85.0) and children were Tri(6.0, 28.1, 58.8). The mean time was configured as a fixed-point distribution Poi(70×365) when calculating carcinogenic risk and as a uniform distribution Uni(ED×365) when calculating non-carcinogenic risk. The skin contact area was configured as a fixed-point distribution, with adult males and adult females configured as Poi(5075) and children configured as Poi(2448). The skin adhesion factor was configured as a fixed-point distribution, with adult males and adult females configured as Poi(0.07) and children configured as Poi(0.2). The particulate emission factor was configured as a fixed-point distribution Poi(1.36×10). 9The adsorption factor was configured as Poi(0.01) when calculating the carcinogenic risk and as Poi(0.03) when calculating the non-carcinogenic risk.

[0176] In addition, soil ingestion rate refers to the mass of soil ingested orally by a population each day; exposure frequency refers to the number of days a population is exposed to contaminated soil each year; exposure duration refers to the number of years a population is exposed to contaminated soil; body weight refers to the body weight of a population; average time refers to the time on which the average exposure amount is calculated; skin contact area refers to the area of ​​skin in contact with soil; skin adhesion factor refers to the mass of soil adhering to a unit area of ​​skin each day; adsorption factor refers to the proportion of heavy metals absorbed through the skin; respiration rate refers to the volume of air breathed by a population each day; and particulate emission factor refers to the soil emission coefficient corresponding to inhalable particulate matter in the air.

[0177] S33. The average daily non-carcinogenic exposure under each exposure route is divided by the corresponding reference dose and then summed to obtain the non-carcinogenic risk index.

[0178] Understandably, for each set of non-carcinogenic average daily exposures obtained from sampling, the non-carcinogenic average daily exposures under the three exposure routes are divided by the reference dose for their respective exposure routes and then summed to obtain the non-carcinogenic risk index for that sampling. The non-carcinogenic risk index is obtained using the following formula.

[0179]

[0180] in, It is a non-carcinogenic risk index. This indicates the three routes of exposure: oral, skin, and respiratory. For the first Average daily non-carcinogenic exposure under various exposure routes For the first The reference doses for each exposure route are specified. For example, the reference doses for the heavy metal arsenic (As) exposure routes of oral ingestion, skin contact, and inhalation can be 0.0003, 0.000123, and 0.000123 mg / (kg·d), respectively.

[0181] The non-carcinogenic risk index refers to the sum of the ratios of the average daily non-carcinogenic exposure to the corresponding reference dose under each exposure route; the reference dose refers to the amount of heavy metals that a person can ingest daily without causing non-carcinogenic health hazards.

[0182] S34. The average daily exposure to carcinogenicity under each exposure route is multiplied by the corresponding carcinogenicity slope factor and then summed to obtain the carcinogenicity risk index.

[0183] Understandably, for each set of average daily carcinogenic exposures obtained from sampling, the average daily carcinogenic exposures under the three exposure routes—oral ingestion, skin contact, and inhalation—are multiplied by the carcinogenic slope factor for their respective exposure routes, and then summed to obtain the carcinogenic risk index for that sampling. The carcinogenic risk index is obtained using the following formula.

[0184]

[0185] in, As a carcinogenic risk index, For the first Carcinogenic slope factors corresponding to different exposure pathways For the first The average daily exposure to carcinogens via various exposure routes. For example, the carcinogenic slope factors for the heavy metal arsenic (As) via oral ingestion, skin contact, and inhalation are 1.5, 3.66, and 15.1 (kg·d) / mg, respectively.

[0186] In addition, the carcinogenic risk index is the sum of the products of the average daily exposure to carcinogens under each exposure route and the corresponding carcinogenic slope factor; the carcinogenic slope factor is the carcinogenic risk coefficient corresponding to a unit of exposure.

[0187] S35, compare the non-carcinogenic risk index and the carcinogenic risk index with the corresponding preset safety thresholds to determine the probability of health risk.

[0188] Understandably, after obtaining multiple sets of non-carcinogenic risk indices and carcinogenic risk indices, each set of non-carcinogenic risk indices is compared with a preset safety threshold. The proportion of times the non-carcinogenic risk index exceeds the corresponding preset safety threshold is calculated out of the total number of samples, yielding the probability of non-carcinogenic risk exceeding the standard. Conversely, each set of carcinogenic risk indices is compared with a preset safety threshold, and the proportion of times the carcinogenic risk index exceeds the corresponding preset safety threshold is calculated out of the total number of samples, yielding the probability of carcinogenic risk exceeding the standard. Preferably, the preset safety threshold corresponding to the non-carcinogenic risk index is 1, and the preset safety threshold corresponding to the carcinogenic risk index is 0.0001. The above calculations are performed on adult men, adult women, and children respectively, yielding the probability of non-carcinogenic risk exceeding the standard and the probability of carcinogenic risk exceeding the standard for each calculation unit for the three groups, thus constituting the health risk probabilities for the three groups across the entire domain.

[0189] S4. Calculate the current environmental capacity based on the risk control benchmark value and the heavy metal content data. Based on the preset annual heavy metal residue rate, extrapolate the current environmental capacity to obtain the overload risk zone.

[0190] Among them, the risk control benchmark value refers to the benchmark concentration determined according to the regional soil environmental quality standards for judging whether heavy metals exceed the standard; the total environmental capacity refers to the total carrying capacity of the soil in the calculation unit for heavy metals; the current environmental capacity refers to the remaining carrying capacity of the soil in the calculation unit for heavy metals at the current moment; and the annual heavy metal residue rate refers to the proportion of heavy metals remaining in the soil each year.

[0191] Understandably, for each calculation unit, the total environmental capacity and the current environmental capacity are calculated based on the risk control benchmark value and the heavy metal content of that calculation unit. The total environmental capacity is then obtained using the following formula. ,

[0192]

[0193] The current environmental capacity is obtained using the following formula. ,

[0194]

[0195] in, The soil weight per hectare is taken as 2.25 × 10⁻⁶. 6 kg / hm², This serves as a benchmark value for risk management of heavy metal elements, for example, the heavy metal element arsenic (As). The value can be 33.5 mg / kg.

[0196] In addition, total environmental capacity refers to the soil's total capacity to hold heavy metal elements; current environmental capacity refers to the soil's remaining capacity to hold heavy metal elements at the current moment.

[0197] In some embodiments, step S4 (deducing the current environmental capacity based on a preset annual heavy metal residue rate to obtain an overload risk zone) includes S41-S42:

[0198] S41, based on the preset annual heavy metal residue rate, risk control benchmark value and heavy metal content data, calculate the predicted environmental capacity of each calculation unit under multiple prediction years.

[0199] Understandably, for each calculation unit, based on the annual heavy metal residue rate, risk control benchmark value, and the heavy metal content of that calculation unit, the calculation for that calculation unit in the [number]th [year] is calculated. The formula for calculating the predicted environmental capacity over a given forecast period is as follows:

[0200]

[0201] in, For the computing unit in the first Predicted environmental capacity over a given forecast period This refers to the annual residue rate of heavy metals. Preferably, the annual residue rate of heavy metals is... The value is 0.9, and the predicted years are... By taking values ​​of 5, 10, and 20 respectively, the predicted environmental capacity for 5, 10, and 20 years is obtained for each calculation unit.

[0202] It should be noted that, It is a value greater than 0 and less than 1.

[0203] S42, select calculation units with current or predicted environmental capacity less than or equal to 0 as overload units, and obtain the area formed by the overload units as the overload risk zone.

[0204] Understandably, for each computing unit, it is determined whether there is a value less than or equal to 0 in the current environmental capacity of the computing unit and the predicted environmental capacity under multiple prediction years. The computing units with values ​​less than or equal to 0 are regarded as overloaded units, and the area formed by all overloaded units is regarded as the overload risk zone.

[0205] In some embodiments, S43 is also included:

[0206] S43. Determine the capacity status and risk level of each computing unit based on the single-element capacity index and the comprehensive capacity index.

[0207] Understandably, after obtaining the overload risk zone, it is possible to further classify the capacity status and risk level of each computing unit based on the single-element capacity index and the comprehensive capacity index.

[0208] The single-element capacity index is obtained using the following formula. ,

[0209]

[0210] The comprehensive capacity index is obtained using the following formula. ,

[0211]

[0212] in, This represents the amount of heavy metal elements.

[0213] Furthermore, the capacity status and risk level of computing units are classified based on the single-element capacity index and the comprehensive capacity index.

[0214] when ≥1 or When the value is ≥1, it is judged as high capacity and risk-free;

[0215] When 0.7 < ≤1 or 0.7< When the value is ≤1, it is judged as medium capacity and low risk;

[0216] When 0.3 < ≤0.7 or 0.3 < If the value is ≤0.7, it is judged as low capacity and medium risk;

[0217] When 0 < ≤0.3 or 0 < When the value is ≤0.3, it is judged as a warning capacity and a severe risk.

[0218] when ≤0 or When the value is ≤0, it is judged as overload capacity and extreme risk.

[0219] It should be noted that the overload risk zone is used to identify computing units that have already or will lose their remaining capacity. Based on this, the capacity status and risk level can be further classified according to the degree of tension of the remaining capacity of computing units that have not yet been overloaded, thereby distinguishing computing units that have not yet been overloaded but are close to being overloaded.

[0220] In addition, the single-element capacity index refers to the ratio of the current carrying capacity of a single heavy metal element to the total carrying capacity; the comprehensive capacity index refers to the average value of the single-element capacity indices of multiple heavy metal elements; the capacity status refers to the soil carrying capacity status determined based on the single-element capacity index and the comprehensive capacity index; and the risk level refers to the level of risk determined based on the single-element capacity index and the comprehensive capacity index.

[0221] It is understood that, based on the heavy metal content data covering the entire target area, this invention assesses the target area from three dimensions: pollution level, health risk, and capacity evolution. The integrated pollution index reflects the current comprehensive pollution level of each calculation unit, the health risk probability reflects the possibility of health hazards of heavy metals to various population groups, and the overload risk zone reflects the capacity evolution results of each calculation unit in the future prediction years.

[0222] Furthermore, the integrated pollution index, health risk probability, and overload risk zone are mapped onto the entire target area according to the center coordinates of the calculation unit, resulting in a spatial distribution map of pollution levels, a spatial distribution map of health risk probabilities for various population groups, and a spatial distribution map of capacity overload early warning. These maps are then compiled into a quantitative decision-making early warning map set. In the quantitative decision-making early warning map set, calculation units with higher pollution levels, higher health risk probabilities, and located within overload risk zones corresponding to the integrated pollution index are designated as priority control units. This provides a comprehensive view of the pollution status, health risks, and capacity evolution of the target area, offering reference information for risk management.

[0223] See Figure 2This is a schematic diagram of a soil heavy metal multidimensional risk coupling assessment and dynamic environmental capacity prediction system provided in an embodiment of the present invention. The system includes:

[0224] The inversion module is used to generate heavy metal content data covering the target area based on remote sensing spectral data and environmental physicochemical covariates of the target area.

[0225] The calculation module is used to calculate the integrated pollution index based on the heavy metal content data;

[0226] The sampling module is used to configure probability distributions for exposure parameters of multiple population groups under various exposure routes, randomly sample each exposure parameter according to the probability distribution, and determine the probability of health risk based on heavy metal content data and the sampled exposure parameters.

[0227] The extrapolation module is used to calculate the current environmental capacity based on the risk control benchmark value and the heavy metal content data, and extrapolate the current environmental capacity based on the preset annual heavy metal residue rate to obtain the overload risk zone.

[0228] See Figure 3 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention. The electronic device 50 includes: a processor 51, a memory 52, and a computer program; wherein...

[0229] The memory 52 is used to store the computer program, and the memory may also be flash memory. The computer program is, for example, an application program or functional module that implements the above method.

[0230] The processor 51 is configured to execute the computer program stored in the memory to implement the various steps performed by the device in the above method. For details, please refer to the relevant descriptions in the preceding method embodiments.

[0231] Alternatively, the memory 52 can be either standalone or integrated with the processor 51.

[0232] When the memory 52 is a device independent of the processor 51, the device may further include:

[0233] Bus 53 is used to connect the memory 52 and the processor 51.

[0234] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, is used to implement the methods provided in the various embodiments described above.

[0235] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0236] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0237] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0238] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for multidimensional risk coupling assessment and dynamic prediction of environmental capacity for heavy metals in soil, characterized in that, include: S1, Based on the remote sensing spectral data and environmental physicochemical covariates of the target area, heavy metal content data covering the target area is generated by inversion; S2, Calculate the integrated pollution index based on the heavy metal content data; including: S21. Based on the heavy metal content data, calculate single-element evaluation indicators for multiple evaluation dimensions, including geoaccumulation index, enrichment factor, pollution factor and ecological risk index. S22, after normalizing the evaluation indicators of each individual element, a weighted sum is performed to obtain the integrated pollution index; including: S221, Based on the respective grading standards of each individual evaluation indicator, determine the risk level and maximum number of levels corresponding to each individual evaluation indicator; S222, after dividing the risk level corresponding to each single-element evaluation indicator by the maximum level number, map it onto a percentage scale to obtain the danger score of each single-element evaluation indicator. S223, determine the weight value corresponding to each single-element evaluation index based on the dispersion of each of the aforementioned hazard scores; including: S2231, Normalize the hazard scores corresponding to each single-element evaluation index to obtain standardized hazard scores; S2232, Based on the standardized hazard score, the information entropy of each single-element evaluation index is calculated using the entropy weight method; S2233, determine the weight value based on the information entropy of each single-element evaluation index; S224. Multiply each hazard score by its corresponding weight value and sum them up to obtain the integrated pollution index. S3, Configure probability distributions for exposure parameters of multiple population groups under various exposure routes, randomly sample each exposure parameter according to the probability distribution, and determine the health risk probability based on heavy metal content data and the sampled exposure parameters; S4. Calculate the current environmental capacity based on the risk control benchmark value and the heavy metal content data. Extrapolate the current environmental capacity based on the preset annual heavy metal residue rate to obtain the overload risk zone. For each computing unit, the total environmental capacity is obtained using the following formula. , The current environmental capacity is obtained using the following formula. , in, The weight of soil per hectare This serves as a benchmark value for risk management of heavy metal elements; The heavy metal content of the current computing unit; This is the background value for the region. include: S41, Based on the preset annual heavy metal residue rate, risk control benchmark value and heavy metal content data, calculate the predicted environmental capacity of each calculation unit under multiple prediction years. For each calculation unit, based on the annual heavy metal residue rate, risk control benchmark value, and the heavy metal content of that calculation unit, calculate the heavy metal content of that calculation unit in the [number of]th [year]. The formula for calculating the predicted environmental capacity over a given forecast period is as follows: in, For the computing unit in the first Predicted environmental capacity over a given forecast period Annual heavy metal residue rate For values ​​greater than 0 and less than 1, predict the number of years. Using values ​​of 5, 10, and 20 respectively, the predicted environmental capacity for 5, 10, and 20 years is obtained for each calculation unit. S42, Select calculation units with current or predicted environmental capacity less than or equal to 0 as overload units, and obtain the area formed by the overload units as the overload risk zone, specifically: For each computing unit, determine whether there is a value less than or equal to 0 in the current environmental capacity and the predicted environmental capacity under multiple prediction periods. The computing units with values ​​less than or equal to 0 are designated as overloaded units, and the area formed by all overloaded units is designated as the overload risk zone.

2. The method according to claim 1, characterized in that, Annual residual rate of heavy metals The value is 0.

9.

3. The method according to claim 1, characterized in that, Before generating heavy metal content data covering the target area based on remote sensing spectral data and environmental physicochemical covariates of the target area, the method further includes: The original hyperspectral image of the target area was acquired as remote sensing spectral data. Pre-set sampling points in the target area were collected, and the collected soil moisture content, pH value and organic matter content were used as environmental physicochemical covariates. Radiometric calibration and atmospheric correction are performed on the remote sensing spectral data to obtain surface reflectance images; The soil abundance of each pixel in the surface reflectance image is determined, and pixels with soil abundance greater than or equal to a preset abundance threshold are extracted as pure soil pixels. The spectral data of the pure soil pixels are extracted, and a spectral feature set is generated based on the spectral data of the pure soil pixels.

4. The method according to claim 3, characterized in that, The inversion generates heavy metal content data covering the target area, including: The spectral feature set is filtered to obtain the characteristic bands that are sensitive to the target heavy metal; The characteristic bands and environmental physicochemical covariates are matrix-concatenated and input into a pre-trained machine learning regression model for inversion, to obtain the heavy metal inversion concentration of each pure soil pixel; The target area is divided into multiple computing units according to a preset spatial grid. The center coordinates of each computing unit and the corresponding heavy metal inversion concentration are extracted to obtain heavy metal content data.

5. The method according to claim 4, characterized in that, Also includes: Determine whether there are any missing heavy metal inversion concentration calculation units in the heavy metal content data; When there are missing computational units for heavy metal inversion concentration, the spatial interpolation algorithm is invoked to complete the heavy metal inversion concentration of the corresponding computational unit.

6. The method according to claim 1, characterized in that, The step of randomly sampling each exposure parameter according to the probability distribution, and determining the health risk probability based on heavy metal content data and the sampled exposure parameters, includes: The exposure routes include oral exposure, skin exposure, and respiratory exposure. Based on heavy metal content data and exposure parameters obtained from sampling, the average non-carcinogenic and average carcinogenic daily exposures of various population groups under oral, skin, and respiratory exposure routes were calculated. The non-carcinogenic average daily exposure to each exposure route is divided by the corresponding reference dose and then summed to obtain the non-carcinogenic risk index. The carcinogenic risk index is obtained by multiplying the average daily carcinogenic exposure amount under each exposure route by the corresponding carcinogenic slope factor and then summing them up. The non-carcinogenic risk index and the carcinogenic risk index are compared with their respective preset safety thresholds to determine the probability of health risk.

7. A soil heavy metal multidimensional risk coupling assessment and dynamic prediction system for environmental capacity, implementing the method of any one of claims 1 to 6, characterized in that, include: The inversion module is used to generate heavy metal content data covering the target area based on remote sensing spectral data and environmental physicochemical covariates of the target area. The calculation module is used to calculate the integrated pollution index based on the heavy metal content data; The sampling module is used to configure probability distributions for exposure parameters of multiple population groups under various exposure routes, randomly sample each exposure parameter according to the probability distribution, and determine the probability of health risk based on heavy metal content data and the sampled exposure parameters. The extrapolation module is used to calculate the current environmental capacity based on the risk control benchmark value and the heavy metal content data, and extrapolate the current environmental capacity based on the preset annual heavy metal residue rate to obtain the overload risk zone.

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