870 results about "Soil heavy metals" patented technology

The invention relates to the technical field of soil pollution detection, in particular to a soil heavy metal pollution intelligent detection method and system.The method comprises the steps that soil multi-source information and regional environment parameters of to-be-detected soil are collected, and soil pollution characteristics are recognized according to the soil multi-source information; establishing a pollution identification analysis model according to the soil pollution characteristics and the regional environment parameters, and analyzing whether the to-be-detected soil contains heavy metal pollutants or not; if the heavy metal pollutants are contained, pollution diffusion paths of the heavy metal pollutants in time and space are predicted according to a pollution identification analysis model; and if the soil does not contain the heavy metal pollutants, performing time sequence analysis on soil pollution characteristics, monitoring and analyzing future concentration distribution of the heavy metal pollutants, and establishing pollution risk early warning. According to the invention, the problems of long sampling analysis time consumption, incapability of dynamically monitoring pollutant concentration change in real time, insufficient diffusion prediction accuracy and difficulty in comprehensively evaluating high-concentration and potential risk areas in a traditional soil heavy metal detection method are effectively solved.

The invention discloses an ecological pollution migration path analysis and early warning method and system based on soil heavy metals, and particularly relates to the technical field of ecological pollution. Monitoring points are arranged around a pollution source, micro CT scanning is combined to analyze permeability changes of soil under different humidity, a synchrotron radiation X-ray spectrometer is utilized to analyze dynamic changes of chemical forms of heavy metal, and the influence of humidity changes on migration paths and form transformation is comprehensively evaluated. The deviation degree of a model prediction result and an actual migration behavior is quantified, early warning signals of different levels are generated for the situation with the high deviation degree, corresponding measures are taken, the complex influence of abnormal soil characteristics on heavy metal migration is dynamically responded, and therefore the harm of soil pollution diffusion to the environment and ecology is effectively prevented and controlled.

The invention relates to the technical field of soil pollution identification, and discloses a soil heavy metal pollution identification system. A multispectral remote sensing sensing module of the system obtains surface reflectance data and soil in-situ spectral data through a satellite load and a vehicle-mounted mobile platform respectively, and the surface reflectance data and the soil in-situ spectral data are processed by a heterogeneous data fusion gateway to generate multiband spectral response signals. In the pollution risk assessment module, a spatial distribution analysis unit outputs a heavy metal spatial distribution map, a migration risk prediction unit generates a pollution migration probability cloud map in combination with meteorological and hydrological data, and a pollution threshold defining unit outputs a soil remediation safety threshold. In the treatment decision execution module, an in-situ remediation execution unit adjusts passivator injection parameters, a pollution source management and control unit regulates pollution source blocking equipment and collects monitoring signals, and a three-dimensional dynamic early warning platform generates a comprehensive pollution risk index. According to the system, the cooperative operation of soil heavy metal pollution identification, evaluation and treatment is realized.

The invention discloses a risk assessment method based on soil heavy metal pollution pathway analysis, and particularly relates to the technical field of land risk assessment. Heavy metal pollutant data and geographical location information of a target area are collected to generate a basic data set; constructing a heavy metal migration model based on the basic data set, and predicting a pollutant migration path and concentration change in combination with the comprehensive characteristics of the regional environment to identify a first high-risk region; a second high-risk area is identified by analyzing retention and release behaviors of pollutants in the microfractures in combination with quantitative soil microfracture connectivity characteristics and pollution migration behaviors of a dynamic entropy model; union processing is carried out on the first high-risk area and the second high-risk area to generate a comprehensive high-risk area; heavy metal concentration and land utilization changes in different time periods of the high-risk area are analyzed and synthesized through a multi-dimensional data fusion algorithm, and cumulative risk distribution is generated; and comparing the accumulated risk distribution with enterprise land use planning, evaluating potential environmental responsibility and generating a risk evaluation report.

The invention provides an industrial park soil and groundwater pollution early warning grade evaluation method and system.The method comprises the steps that production raw material components of pollution source enterprises, waste discharge data and soil heavy metal concentration evolution data in historical pollution events are integrated, optical sensing data of earth surface and underground monitoring points are combined, and the early warning grade of soil and groundwater pollution is evaluated; constructing a dynamic characteristic factor library reflecting pollution source characteristics; carrying out spatial concentration gradient calculation on the optical sensing data based on the characteristic factor library, and generating a dynamic heat map containing heavy metal distribution and migration trends at the same time; performing cross-modal analysis on the pollution source characteristics and the dynamic heat map, and establishing a spatio-temporal evolution trajectory of a pollution diffusion path; according to the diffusion rate and direction in the track, differentiated pollution risk early warning intervals are divided, and a targeted grading prevention and control strategy is generated. According to the invention, the timeliness of pollution early warning and the targeting of prevention and control measures are improved.

The invention discloses a field soil heavy metal pollution health risk dynamic assessment and intelligent early warning system, and relates to the technical field of soil environment monitoring. The system comprises a multi-source data acquisition unit, a data preprocessing unit, a risk calculation engine and a visual interaction terminal. The key technical point is that a dynamic field evolution analysis module and an adaptive grid rendering control module are introduced; the dynamic field evolution analysis module constructs a pollution potential energy field matrix representing a pollutant migration trend based on soil heavy metal concentration and hydrogeological parameters, and calculates a space-time gradient change vector of the pollution potential energy field matrix; and the latter dynamically adjusts the grid local density according to the gradient vector module value, and automatically encrypts the computational nodes in the region with severe risk change. In cooperation with a time sequence prediction deduction and feedback correction mechanism, the method can simulate the dynamic evolution of the pollution plume in the porous medium in real time, solves the problems that a migration rule is difficult to capture and the calculation efficiency of a uniform grid is low in traditional static evaluation, and achieves three-dimensional dynamic risk early warning with high precision and low calculation power consumption.

The invention relates to the technical field of spectral data processing, and provides a soil heavy metal content detection and correction method based on an X-ray fluorescence spectrum, which comprises the following steps: acquiring a plurality of XRF spectral signals of a to-be-detected soil sample as an original spectral curve; obtaining the overall similarity of each original spectrum curve and the initial interference weight of each peak point in the original spectrum curves; decomposing to obtain a plurality of component signals; constructing a plurality of local windows of each component signal; obtaining a noise interference factor of each local window in each original spectrum curve; obtaining an adjustment reference coefficient of each local window in each original spectrum curve; obtaining a filtering adjustment coefficient of each wavelength in each original spectrum curve; dynamically filtering and smoothing the original spectrum curve to obtain a to-be-measured spectrum signal; and detecting the soil heavy metal content based on the to-be-detected spectral signal. The invention aims to solve the problem of heavy metal content detection deviation caused by noise in an X-ray fluorescence spectrum.

The invention relates to the technical field of in-situ / ex-situ remediation of sites polluted by heavy metals such as cadmium, lead, copper and zinc, and discloses a heavy metal polluted soil remediation system and method based on intelligent management and control. The system comprises an electrode system module, an activator module, a targeted adsorption device module, an automatic monitoring system module, an intelligent decision-making system module and a photovoltaic power supply module. The method comprises the steps of driving heavy metal ions in soil to directionally migrate through an electric field; the activating agent module works, green micromolecule organic acid and other activating agents are injected in a gradient mode, and the migration efficiency of heavy metal is improved; activated heavy metal ions can be captured by an adsorption material in the targeted adsorption device module in cooperation with a selective enrichment device; meanwhile, the automatic monitoring system module collects environmental parameters in real time; the intelligent decision-making system module optimizes the remediation efficiency and energy consumption according to the real-time data, and low-cost and high-efficiency remediation of soil heavy metal pollution is achieved. The remediation efficiency is remarkably improved, and the risk of secondary pollution is reduced.

The invention relates to a heavy metal content quantitative inversion method and system combining a GF-5 remote sensing image and a ground soil hyperspectrum, and the method comprises the steps: obtaining a bare soil image based on a normalized vegetation index value of the GF-5 remote sensing image, and carrying out the DS correction and first-order derivative spectrum transformation of the bare soil image; obtaining ground hyperspectral reflectivity data, soil heavy metal content and remote sensing image hyperspectral reflectivity data of the sampling points; dS correction and first-order derivative spectrum transformation are carried out on the hyperspectral reflectivity data of the remote sensing image; screening remote sensing image hyperspectral reflectivity data after first-order derivative spectral transformation through a Boruta algorithm, and constructing a heavy metal inversion model in combination with the soil heavy metal content; and predicting the bare soil image after the first-order derivative spectral transformation based on a heavy metal inversion model to obtain a heavy metal content pollution spatial distribution diagram. The soil heavy metal pollution condition in a large range can be rapidly and accurately monitored, and a basis is provided for ecological restoration.

The invention discloses a method for predicting the content of heavy metals in different occurrence forms in soil based on machine learning, and relates to the technical field of soil component content analysis, and the method comprises the following steps: (1) collecting soil physicochemical properties, the total amount of soil heavy metals, the types of the heavy metals in the soil and the content of the heavy metals in different occurrence forms in the soil from a literature library, constructing a sample database; (2) preprocessing the sample database to obtain a preprocessed sample database; (3) for each kind of heavy metal, extracting corresponding sample data from the preprocessing sample database, dividing the sample data into a training set and a test set, selecting a machine learning algorithm, establishing a prediction model training model of different occurrence form contents of the heavy metal in the soil, and optimizing hyper-parameters in the training process; and (4) predicting the content of heavy metals in different occurrence forms in the soil to be detected by using the optimized prediction model. The method has a good application prospect in the aspects of heavy metal pollution risk evaluation and the like.

The invention discloses a cumulative environmental risk early warning analysis method and system for soil heavy metal pollution, particularly relates to the technical field of soil environment monitoring, and is used for solving the problems that the existing risk assessment of leakage from heavy metal pollution to underground water is incomplete and insufficient in accuracy. Heavy metal concentration depth distribution data are obtained by measuring heavy metal concentrations in soil at different depths; judging whether a heavy metal pollution risk influencing underground water exists or not according to the data; when the risk exists, analyzing the dynamic influence of surface charge change of soil particles on heavy metal adsorption and desorption behaviors by adopting Gaussian process regression, analyzing the retention effect of the heavy metal in a fracture network through a random walk model, and evaluating the delay influence of the retention effect on the migration rate and path of the heavy metal; and finally, according to the generated heavy metal underground leakage risk assessment result, the groundwater pollution risk is graded, and the accuracy of groundwater pollution risk assessment can be effectively improved.

The invention provides an intelligent early warning method and system for phosphorus tailing improved soil heavy metal migration, relates to the technical field of computers, and accurately predicts the main migration direction of heavy metal pollutants by combining factors such as terrain gradient, hydrological flow direction and soil structure heterogeneity. The method comprises the following steps: acquiring soil information of a to-be-detected area and soil potential information of a detection point in the to-be-detected area; determining heavy metal concentration information of the detection point according to the soil potential information; the heavy metal concentration information is used for reflecting heavy metal concentration distribution conditions corresponding to different soil depths of the detection points; determining migration direction information of the heavy metal at the detection point; the migration direction information is used for reflecting the corresponding heavy metal migration direction distribution condition of the heavy metal of the detection point in the three-dimensional space; and predicting a pollution diffusion area based on the migration direction information, the organic decomposition rate and the underground water level fluctuation information, and performing visual display.

The invention provides a soil heavy metal pollution ecological risk assessment method based on machine learning and application, and relates to the field of ecological risk assessment. According to the method, a multi-index data set is constructed through a constructed metal data set and corresponding pollution load indexes, Neimero indexes, potential ecological risk indexes, geological accumulation indexes and improved biomarker indexes, coupling weight determination and dimension reduction processing are achieved by means of PCA, machine learning modeling is combined, and a multi-index data set is constructed. And constructing a soil heavy metal multi-classification ecological risk assessment model so as to carry out heavy metal pollution ecological risk assessment on the target soil. The method combines the advantages of multiple biological and non-biological indexes, solves the limitation of a single index in coping with complex pollution conditions, and has high accuracy. When the method is applied to grading planning of the soil heavy metal pollution area, the soil heavy metal pollution area can be reasonably controlled and delimited efficiently and accurately; the method is high in applicability and suitable for large-area popularization and application.

The invention relates to a soil element hyperspectral inversion method based on CARS-IRIV waveband selection, and belongs to the field of soil hyperspectral prediction. The method comprises the following steps: firstly, collecting a soil sample, measuring visible near-infrared hyperspectral data and chromium concentration of the soil sample, removing wavebands with relatively large noise in the hyperspectral data, dividing a data set into a training set and a verification set, preprocessing the training set and the verification set, and then extracting characteristic wavebands by adopting CARS-IRIV; and then establishing a soil chromium concentration prediction model of support vector regression, training the model by using a training set, evaluating prediction precision of the model by using a verification set in combination with evaluation indexes, and finally drawing a soil element chromium concentration spatial distribution diagram in a sampling area. According to the method, the soil elements can be quickly and accurately inverted, and powerful technical support is provided for quantitative inversion of the concentration of the heavy metal chromium in the soil.

The invention discloses a soil heavy metal content estimation method and device, and relates to the technical field of soil heavy metal estimation.The method comprises the steps of sample collection and data acquisition, specifically, based on geographic information of a research area, soil samples are collected through a layered and partitioned sampling method; according to the method, comprehensive and accurate collection and data acquisition of soil samples are realized through a layered and partitioned sampling method in combination with a high-precision positioning and measuring instrument, the sample representativeness and the data accuracy are ensured, and through an improved correlation analysis algorithm and a depth model based on feature fusion, the soil sampling accuracy is improved. Accurate analysis and efficient modeling of correlation between environmental magnetic parameters and heavy metal content are realized, prediction precision is improved, dynamic optimization of model parameters is realized by applying a self-adaptive step length strategy in model verification and optimization, model stability and generalization ability are guaranteed, and all modules of the device work cooperatively, so that the reliability of the device is improved. And deep analysis of heavy metal sources and migration and transformation rules is realized by using a geochemical analysis module and related models.

The invention discloses a protein-based hydrogel adsorption material and application thereof in repairing water and soil heavy metal pollution, polysaccharide is doped into protein hydrogel to prepare the hydrogel adsorption material which is good in toughness, high in porosity and rich in surface adsorption functional groups. The heavy metal contaminated water and soil can be effectively adsorbed and efficiently recycled, especially for the heavy metal contaminated soil, the heavy metal contaminated soil is coated with the heavy metal contaminated water and soil adsorbent, the heavy metal contaminated soil is covered with the heavy metal contaminated water and soil adsorbent, in-situ remediation of the contaminated soil is achieved, meanwhile, the thin film is removed after remediation, and the risk of secondary overflow is thoroughly avoided. Results show that the protein-based hydrogel adsorbing material can efficiently remove different heavy metal pollutants, and after different treatments, the corresponding agricultural soil land control level can be reached. Moreover, no nutrient loss is observed in the treated soil, and no secondary toxic product is generated, so that the method has relatively high sustainability and agricultural application feasibility.

The invention provides a soil heavy metal pollution risk zoning and grading method based on machine learning. The method comprises the following steps: respectively acquiring heavy metal sample data and environmental covariant data in a research area as a corresponding initial sample data set of target heavy metal; and preprocessing each initial sample data set to obtain a corresponding research sample data set. And obtaining pollution risk indexes of the target heavy metals. Mixing the research sample data sets, and randomly dividing the research sample data sets into a training set and a test set; and establishing a heavy metal risk zoning and grading model based on a random forest model according to the classification labels, the training set and the test set. And according to the effective environmental influence variables corresponding to the unknown areas in the research area, identifying and partitioning the soil heavy metal pollution risk of the research area. According to the method, the soil heavy metal pollution risk is quantitatively, qualitatively and from multiple angles, and the soil heavy metal potential pollution risk area is scientifically divided.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

The invention discloses a preparation method and application of monatomic iron-doped carbon quantum dot nano enzyme, and the preparation method comprises the following steps: fully dispersing citric acid, urea and iron salt, and fully reacting in a closed container; the ferric salt is any one of ferric citrate, iron methionine or ferrous lactate. The monatomic iron-doped carbon quantum dot nano-enzyme provided by the invention is good in dispersity, excellent in water solubility and uniform and stable in structure, has efficient antioxidant enzyme activity, can be easily absorbed by plants, can remarkably improve the antioxidant capacity and stress resistance of the plants after being applied, can remove excessively accumulated active oxygen of the plants, and also can effectively inhibit the growth of the plants. The soil heavy metal pollution control agent can control soil heavy metal pollution, also can improve the removal efficiency of antioxidant enzymes in plants, reduce heavy metal absorption and increase the plant yield, has the characteristics of small dosage, low cost, high efficiency, high environmental safety and easy use, and provides a new technology and product choice with practical application value for treatment of soil heavy metal pollution.

The invention provides a method for jointly repairing a coal mine wasteland by using plants and microorganisms, and aims to promote vegetation recovery, reduce water and soil loss, reduce leaching water pollutants and soil heavy metal content, improve treatment efficiency and effectively improve the ecological environment of the coal mine wasteland through planting of Juncao and using of Juncao nitrogen-fixing bacterial fertilizer. Reutilization of the waste land is achieved, and a new thought and approach are provided for treatment of the coal mine waste land.

The invention provides a mining area surrounding soil heavy metal spatial distribution inversion method based on hyperspectral data, and the method comprises the steps: processing multi-source monitoring data of mining area surrounding soil, and obtaining a consistent reflectivity data set; performing soil spectrum purification based on the consistent reflectivity data set to obtain a pure soil signal; spectrum key features are screened out from the pure soil signals; obtaining a modeling data set in combination with the spectrum key features and the heavy metal concentration labels so as to construct a multi-task inversion model, and outputting each metal prediction interval and an over-standard risk probability graph; and outputting a multi-layer package based on the model, performing global and local interpretation and mechanism verification, and generating an interpretation report and traceable evidence. According to the method, full-link unification and purification can be achieved, domain deviation and mixed pollution are remarkably reduced, the accuracy and interpretability of feature screening can be considered, and the model generalization ability, prediction accuracy and space credibility are improved.

The invention discloses a heavy metal infiltration risk prediction method for reclaimed water irrigation mining and metallurgy area greenbelt soil, which comprises the following steps: detecting the contents of heavy metals and salt ions in mining and metallurgy area reclaimed water and greenbelt soil, and detecting the physicochemical properties and hydraulic parameters of the soil; carrying out experimental simulation on the basis of detection data to obtain heavy metal form distribution and chemical equilibrium parameters of each phase of soil, then constructing a soil mineral phase-organic matter heavy metal adsorption kinetic model, constructing a soil solid phase equilibrium adsorption capacity calculation model, and further constructing a reclaimed water driven heavy metal geochemical migration process model; the heavy metal content of the mining and metallurgy area soil serves as an initial input item of the migration process model, and the heavy metal infiltration flux of the mining and metallurgy area greenbelt soil in the target prediction time is predicted in a rolling mode according to the heavy metal concentration of the recycled water; and calculating the heavy metal pollution index according to the predicted heavy metal infiltration flux. The method can quickly, accurately and economically evaluate the heavy metal infiltration risk of the reclaimed water irrigated green land soil.

The invention discloses a method for restoring soil heavy metal pollution by coupling soybean urease with a charcoal cementing agent, and relates to the technical field of soil heavy metal pollution restoration, and the method comprises the following steps: dynamic microbiome regulation and control: introducing photosensitive protein into soil functional bacteria; the acyl homoserine lactone analogue is synthesized and applied to soil, and is competitively combined with a microbial quorum sensing receptor to block an inhibitor to secrete a signal channel; according to the method, microbial urease secretion is regulated and controlled through optogenetics, an inhibitor signal is blocked through quorum sensing interference, and a microbiome is precisely regulated and controlled; the pH-responsive nanocapsule releases an inhibitor in a targeted manner, and the magnetic-responsive nanochain dynamically captures interfering bacteria, so that the limitation of passive adsorption is broken through; the gene editing plant releases coumarin to inhibit fungal metabolism, and the engineering mycorrhizal fungi physically shields an inhibitor to construct cross-biosphere cooperative defense; the urease activity and the heavy metal removal rate are remarkably improved, meanwhile, the soil microbial diversity is maintained, and the soil specificity interference problem is solved.

The present application relates to the technical field of heavy-metal pollution remediation, and relates in particular to a soil heavy‐metal accumulation predicting method employing an emission inventory and a receptor model. The method comprises: using atmospheric dust deposition, irrigation water, chemical fertilizers and pesticides as input flux sources of heavy metals to farmland soil, using surface runoff and crops as output fluxes, and constructing an input-output flux inventory of heavy-metal pollutants to clarify a dynamic balance relationship of soil heavy-metal accumulation; and continuously collecting and monitoring of soil samples. The invention provides crucial assistance in determining the transport balance of heavy metals in farmland areas impacted by non-ferrous metal smelting and beneficiation slag sites and in identifying sources of soil heavy-metal pollution, and offers theoretical guidance for subsequent soil heavy-metal remediation and targeted control.

The invention relates to halophilous bacillus (Halophilous sp.) HMF09, and the preservation number of the halophilous bacillus is CGMCC (China General Microbiological Culture Collection Center) No.30475. The strain has high saline-alkaline tolerance and efficient cadmium ion immobilization capability, and can be applied to reduce the ion activity of heavy metal cadmium in soil, reduce stress of saline-alkaline on crops and promote crop growth.

The invention relates to acid-resistant sulfate reducing bacteria and application thereof, and belongs to the technical field of microorganisms. The acid-resistant sulfate reducing bacterium disclosed by the invention is Desulfovibrio desulfuricans QY411, and is preserved in the Guangdong Microbial Culture Collection Center on March 14, 2024, and the preservation number of the acid-resistant sulfate reducing bacterium is GDMCC No: 64412. The acid-resistant sulfate reducing bacterium provided by the invention can survive in an environment with the pH value of 4, and can be applied to ecological reconstruction of acid tailings and waste dump soil, the pH values of mining wasteland soil and surface water after ecological reconstruction are remarkably increased, the concentration of SO4 < 2-> in the soil is remarkably reduced, the acidification process in the mining wasteland soil is effectively inhibited, and the acid-resistant sulfate reducing bacterium has the advantages of being safe and reliable. Therefore, the acidification process in the mining wasteland is effectively controlled. Meanwhile, soil heavy metal pollution is reduced, and vegetation growth is recovered.

The invention relates to the technical field of soil detection, and particularly discloses a rapid soil heavy metal pollution detection method based on a spectrum technology, which comprises the following steps: obtaining dual-band spectral reflectivity data and temperature and humidity parameters of to-be-detected soil, carrying out spectral characteristic compensation, generating a spectrum curve, and obtaining environment compensation parameters; extracting spectral characteristic parameters from the spectral curve, combining the spectral characteristic parameters to construct a multi-dimensional spectral characteristic matrix, and inputting the multi-dimensional spectral characteristic matrix into a CNN-RF hybrid model for soil heavy metal pollution detection to obtain a heavy metal pollution detection result. According to the method, in-situ lossless rapid detection of the soil heavy metals is achieved through the dual-band spectrum and the environment dynamic compensation technology, interference of environmental factors on spectral measurement is effectively reduced, the detection efficiency is improved, the pollution condition of the heavy metals in the soil is accurately recognized through the CNN-RF mixed model, the reliability and precision of soil heavy metal pollution are improved, and the method is suitable for popularization and application. Corresponding treatment measures can be taken in time, and the safety of the soil environment is guaranteed.

The invention discloses a farmland soil heavy metal inversion method fusing spectrum layering and machine learning, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a to-be-measured spectral variable of soil in a survey area; and inputting the to-be-detected spectrum variable into the soil heavy metal inversion model, generating a heavy metal prediction concentration spatial distribution diagram of the investigation area output by the soil heavy metal inversion model, and constructing the soil heavy metal inversion model based on the calibration sample set and the verification sample set. The calibration sample set and the verification sample set perform stratified sampling determination on a plurality of training samples based on soil component information of sample soil, and the training samples comprise important spectral variables and heavy metal concentrations of the sample soil; and determining a heavy metal concentration spatial distribution diagram of the farmland in the investigation area according to the heavy metal predicted concentration spatial distribution diagram. According to the method, the accuracy of soil heavy metal concentration monitoring is remarkably improved, and the monitoring cost and time are effectively reduced.

The invention discloses a soil heavy metal distribution prediction method and system based on machine learning, and the method comprises the steps: obtaining topographic factor land use industrial activity data, carrying out the fusion remote sensing information processing, and determining a multi-source data set; performing standardization processing according to the multi-source data set, and performing space-time registration if the scale difference after standardization processing exceeds a preset threshold value to obtain data in a unified format; key features are extracted according to the unified format data, and a dimension reduction feature set is obtained through principal component analysis; a random forest model is constructed according to the dimension reduction feature set, parameters are optimized, and a heavy metal content prediction model is determined; inputting the sampling finite point location data into a heavy metal content prediction model, judging an industrial activity influence area, and obtaining a preliminary distribution estimation result; based on the preliminary distribution estimation result, high-resolution grid data are obtained by fusing topographic factors for the space complex region; and generating a pollution distribution diagram according to the high-resolution grid data, judging a low-prediction-precision region, and obtaining a final optimized layer.

The invention discloses a soil heavy metal chelating agent, the chelating agent comprises a chelating agent I and a chelating agent II, the chelating agent I is composed of fly ash and domestic sludge, and the chelating agent II is a modified sodium alginate compound; the mass ratio of the chelating agent I to the chelating agent II is (15-20): (3-5). According to the invention, the chelating agent I is composed of the fly ash and the domestic sludge, the modified sodium alginate compound is used as the chelating agent II, and the two chelating agents are cooperatively used in the soil reclamation of the strip mine dump, so that a series of environmental problems caused by mining activities can be effectively improved, and the reasonable utilization of land resources is promoted.