685 results about "Soil heavy metals" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of soil heavy metal pollution detection, and discloses a soil heavy metal pollution detection method and system, and the method comprises the steps: collecting an original signal containing a target heavy metal characteristic signal and a plurality of auxiliary background signals; preprocessing the original signal to obtain a preprocessed multi-dimensional signal; analyzing the multi-dimensional signal to identify interference characteristics of the target heavy metal characteristic signal and the auxiliary background signal; according to the interference characteristics of the auxiliary background signal, evaluating the composition and intensity of background interference, and according to the composition and intensity of the background interference, separating a pure signal of the target heavy metal from the background by using a nonlinear compensation algorithm; the target heavy metal pure signal is converted into the concentration value of the target heavy metal, chemical digestion is not needed, complex background interference can be effectively recognized and compensated for for soil heavy metal pollution detection, the accuracy and reliability of field detection are remarkably improved, and errors caused by inaccurate reagent degradation and background deduction in a traditional method are overcome.

The invention relates to a real-time monitoring method for soil remediation by utilizing multi-spectrum of an unmanned aerial vehicle and sensing coupling of the Internet of Things, and belongs to the technical field of soil environment monitoring and remediation. The method comprises the following steps: constructing a space grid model of a monitoring area; a space-air-ground integrated monitoring network is arranged, image data are obtained through multi-spectral remote sensing of an unmanned aerial vehicle, and soil environment parameters are collected through a ground Internet of Things sensor; preprocessing and fusing the multi-source spatio-temporal data, and establishing a spatio-temporal matching model; constructing an inversion model of the soil heavy metal content, the organic matter content and the pollutant degradation degree based on the fusion data; and the repair efficiency is dynamically calculated and visualized, and real-time evaluation and early warning of the repair process are realized. According to the invention, space-air-ground data collaboration is realized, the real-time performance, accuracy and space coverage of monitoring are remarkably improved, the defects of high cost, low efficiency and limited data dimension of a traditional method are overcome, and whole-process and intelligent decision support is provided for soil remediation.

The invention relates to the technical field of soil pollution analysis, and discloses a soil heavy metal intelligent evaluation method, system and terminal, and the method comprises a multi-source heterogeneous data fusion and preprocessing step, a dynamic spatial interpolation modeling step and an intelligent visualization and decision support step. The system corresponds to the method, and the terminal equipment corresponds to the system. According to the method, comprehensive data support for soil heavy metal pollution is realized through multi-source heterogeneous data fusion and preprocessing; through dynamic spatial interpolation modeling and a dynamic parameter adjustment mechanism, high-precision grid surface layer generation is realized, and the fitting precision of pollution spatial distribution is improved; intelligent pollution grading and multi-factor dynamic calculation of pollution indexes are realized through self-adaptive grading and pollution index calculation, and the accuracy of an analysis result is improved; through intelligent visualization and decision support, a dynamic and visual decision basis is provided for multi-department collaborative governance and risk early warning.

The invention provides an acid soil conditioner and a preparation method thereof, and belongs to the technical field of soil conditioners. The soil conditioner is prepared from the following raw materials in parts by weight: 30-40 parts of pH-sensitive hydrogel, 3-7 parts of slow-release nitrogen-fixing bacteria microcapsules, 5-8 parts of soil improvement bacteria, 10-20 parts of a chitosan / sodium alginate modified compound, 17-20 parts of fly ash and 18-22 parts of phosphogypsum. According to the prepared acid soil conditioner, the microorganism and chemical improvement effect is adopted, the pH value of soil can be obviously increased, the granular structure of the soil is improved, the buffering capacity of the soil is enhanced, the fertility of the soil is improved, the heavy metal content of the soil is reduced, the quality of the soil is improved, and the crop yield is increased, so that the acid soil conditioner has wide application prospects.

The invention discloses a method and a device for treating soil heavy metal pollutants by an electroosmosis technology, and relates to the technical field of soil pollution treatment and remediation, and the method comprises the following steps: constructing an electroosmosis system, a liquid injection system and a pumping drainage system; liquid injection and pumping drainage are conducted, electroosmosis enhancer liquid medicine is injected into the soil, and meanwhile the liquid is pumped drainage till the concentration of the electroosmosis enhancer in the pumped drainage liquid reaches the preset requirement; soaking and electroosmosis are carried out; sampling detection is conducted, and liquid pumping sampling is conducted on the soil in the to-be-treated area till the pollutant residual concentration meets the preset requirement; water injection and pumping drainage are conducted, clear water is injected into the soil, meanwhile, liquid is pumped drainage, and soil treatment is finished until the concentration of the electroosmosis enhancer in the pumped drainage liquid is smaller than or equal to the preset concentration limit value. According to the method, a proper amount of electroosmosis enhancer liquid medicine is injected, heavy metal pollutants are dissociated through the electroosmosis effect and pumped and discharged to the hazardous waste pool, and heavy metal in soil is efficiently and reliably reduced.

The invention discloses a soil heavy metal content inversion method combining VIS-NIR and XRF spectrums, which comprises the following steps: firstly, collecting the VIS-NIR and XRF spectrums of a soil sample to be detected in a laboratory, and preprocessing the spectral data; then, a competitive self-adaptive reweighted sampling algorithm is adopted to screen characteristic wave bands, and a data set is divided into training data and verification data; then, adopting a partial least square method to respectively construct soil heavy metal content estimation models of different spectrum types and different spectrum preprocessing methods, and further evaluating a dominant single spectrum model; and finally, fusing the characteristic wave bands of the VIS-NIR and XRF spectrums of the dominant model, and constructing a soil heavy metal content inversion model based on spectrum fusion. The precision of a soil heavy metal content inversion model can be improved by combining the VIS-NIR spectrum and the XRF spectrum, the problem that the soil heavy metal content monitoring precision is difficult to meet by independently using the VIS-NIR spectrum and the XRF spectrum is solved, and a technical means is provided for rapidly estimating the heavy metal content based on multi-source spectrum fusion.

The invention relates to the technical field of soil heavy metal pollution remediation, in particular to a heavy metal cadmium pollution remediation neural network model, which comprises the following steps: S1, acquiring and preprocessing environmental factor data; s2, constructing a cadmium content prediction model: establishing an initial model structure, carrying out model training based on the preprocessed environmental factor data, calculating the contribution degree of each environmental factor in the model by utilizing an interpretability analysis method, and constructing an optimization objective function with prediction precision and model complexity by taking the environmental factor contribution degree as an interpretability constraint; performing iterative optimization on the model structure based on the optimization objective function to obtain a cadmium content prediction model; and S3, predicting the cadmium content of the rice based on the cadmium content prediction model, and optimizing agricultural measures based on a prediction result. By means of the scheme, accurate prediction and scientific explanation of the cadmium content of the rice are achieved, and the practicability of the model in farmland heavy metal cadmium pollution remediation is improved.

The invention provides a calcium humic acid modified biochar composite material as well as a preparation method and application thereof. The preparation method specifically comprises the following steps: preparing calcium modified biochar powder from biochar powder and a calcium source; mixing humic acid with the calcium modified charcoal powder, filtering and drying to obtain a calcium humic acid modified charcoal composite material; wherein the mass ratio of the humic acid to the calcium modified charcoal powder is 1: (2-20). When the calcium humic acid modified biochar composite material is used as a soil remediation agent for soil remediation, the pH value, the organic matter content and the cation exchange capacity of soil can be improved, the soil hardening degree is relieved, heavy metal lead and cadmium and ions thereof can be immobilized in the modes of ion exchange, electrostatic adsorption, surface complexing, deposition and the like, and the soil remediation efficiency is improved. The soil heavy metal content is further reduced.

A method and system for tracing the source of non-point source heavy metal pollution in a watershed, the method first determines the spatial distribution of soil heavy metal content under various land use types in the watershed, the sediment enrichment ratio and water solubility factor of soil heavy metals, and simulates the spatial and temporal distribution of rainfall runoff and soil erosion sand production, and then calculates the loss of land surface heavy metal pollution, and then combines the migration of heavy metal pollution in the river channel to calculate the loss of non-point source heavy metal pollution in the outflowing river section under various land use types, so as to obtain the contribution ratio of each land use type to the non-point source heavy metal pollution in the watershed, and finally determines the main source of the loss of heavy metal pollution in the watershed according to the contribution ratio. The present invention comprehensively considers the impact of land loss and river migration process on non-point source heavy metal pollution in the watershed, and can realize the accurate calculation of the loss load of non-point source heavy metal pollution at the watershed scale and the rapid and precise identification of pollution sources.

The invention relates to the field of soil pollution treatment, and discloses a preparation method and application of a composite soil conditioner capable of improving the carbon sequestration effect, and the preparation method of the soil conditioner comprises the following steps: step A, preparing biochar; step B, preparing biochar microalgae particles; step C, preparation of oyster shell powder; d, coating particles to obtain an inner-layer soil conditioner and an outer-layer soil conditioner; the soil conditioner comprises an inner layer and an outer layer which are formed by adhesion of auxiliary materials, the inner layer comprises microalgae particles enriched and recycled by charcoal and can improve acid soil for a long time and provide organic matter and nitrogen and phosphorus nutrition, and the outer layer comprises roasted oyster shell powder and is rapid in soil acidity adjustment and capable of providing calcium salt. The composite soil conditioner has a synergistic effect of various materials, can be rapidly and slowly combined, can effectively improve acid soil, passivate soil heavy metals, provide organic matters, calcium, nitrogen, phosphorus and other nutritive salts for a relatively long time, improve the nutritional quality of vegetables and improve the carbon sequestration effect of soil, and has relatively good comprehensive benefits.