1441 results about "Heavy metals pollution" patented technology

The invention discloses a composite interference-oriented cultivated land system toughness evaluation and optimization method, and relates to the technical field of cultivated land toughness optimization, and the method comprises the steps: collecting and preprocessing multi-source cultivated land data, constructing a toughness evaluation index system, and calculating the weight of each index; and calculating a comprehensive toughness score based on weight distribution, drawing a time sequence change curve based on the score, and performing region division to generate a toughness distribution diagram. According to the method, the single-factor pollution index, the Nemo comprehensive index and the interaction effect index of heavy metal pollution are combined, the soil pollution condition is accurately quantified through a weighted summation method, a scientific and reliable basis is provided for toughness evaluation, the dynamic toughness distribution diagram is generated in combination with the LSTM model and the GIS technology, the spatio-temporal dynamic characteristics of a cultivated land system under composite interference are revealed, and the method has the advantages of being high in reliability and high in reliability. According to the method, the defects of an existing model in space-time analysis are overcome, differentiated optimization strategies are formulated based on region division and weak region labeling, and the resource allocation efficiency and the pertinence of an optimization scheme are remarkably improved.

The invention discloses a water quality heavy metal pollution detection method and system based on Raman spectrum.The water quality heavy metal pollution detection method comprises the steps that water body Raman scattering light is collected in situ through a miniature optical fiber probe, and a continuous time sequence spectrum signal flow is generated; wavelet transform is combined with self-adaptive threshold setting, and high-frequency noise and effective spectral signals are separated; dynamically strengthening the characteristic peak of the target heavy metal through a frequency domain characteristic screening module, and inhibiting a water molecule interference peak at the same time; generating a cross-domain fusion feature vector; processing the fusion feature vector through a pre-trained heavy metal concentration prediction model, and outputting concentration prediction values of various heavy metals; a confidence score is dynamically calculated based on a deviation between a current predicted value and historical data distribution, and model parameter update and system calibration are automatically triggered. The method has the advantages that through acousto-optic signal cross-domain fusion and closed-loop self-calibration, the target peak is dynamically strengthened while water molecule interference is inhibited, and the real-time performance, the anti-interference performance and the prediction precision of heavy metal detection are improved.

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 a water quality heavy metal pollution detection method and system based on Raman spectrum. The method comprises the following steps: collecting a water sample through a water area to be detected, and scanning the water sample by adopting a Raman spectrometer to generate an original Raman spectrogram; based on the original Raman spectrogram, using a wavelet transform algorithm to perform de-noising processing, reducing background interference, using a principal component analysis technology to extract characteristic peaks, and generating a heavy metal preliminary prediction result; based on the preliminary heavy metal prediction result, constructing a heavy metal concentration prediction model by applying a partial least squares regression algorithm, performing quantitative analysis, evaluating prediction accuracy by adopting a cross validation technology, and generating a heavy metal actual concentration result; and based on the actual heavy metal concentration result, comparing with a water quality safety standard, evaluating a pollution condition, and generating a water quality heavy metal pollution detection report. The technical scheme provided by the invention has high sensitivity, can efficiently denoise and accurately predict, and provides a scientific means for monitoring and treating heavy metal pollution of water.

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 discloses a growth-promoting bacillus velezensis FSUXF-717 capable of tolerating heavy metals and application of the growth-promoting bacillus velezensis FSUXF-717, and belongs to the technical field of agricultural microorganisms. The bacillus velezensis FSUXF-717 has the following advantages: (1) the bacillus velezensis FSUXF-717 has multifunctional metabolic characteristics: various carbon sources such as cane sugar, raffinose and aesculin can be utilized, and the bacillus velezensis FSUXF-717 has the functions of producing long-chain hormone, dissolving phosphorus, secreting ACC deaminase, efficiently forming a biological membrane and the like; (2) broad-spectrum adaptability: the strain has strong tolerance to heavy metal pollution (Cd / Pb / Cr / Ni), drought and broad-spectrum acid-base stress (pH 4.5-9.5); under the condition of relatively low bacterium concentration, the auxin can be stably synthesized in a heavy metal-acid-base composite stress environment. And (3) the activity of soil sucrase and soil phosphatase is enhanced. Excavation of the strain can provide an important germplasm resource basis for development and utilization of novel efficient microbial fertilizers and lay a foundation for green and high-quality development of agriculture.

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 the technical field of land improvement, in particular to an alginic acid type multifunctional long-acting soil conditioner and a preparation method and application of the alginic acid type multifunctional long-acting soil conditioner. The core particle layer comprises an organic material, nano silicon, calcium magnesium polyphosphate, magnesium hydride, ammonium borate, rhamnose and an adhesive, and the slow release layer comprises clay minerals and alginate; the mass ratio of the clay mineral to the alginate to the organic material to the calcium magnesium polyphosphate to the nano silicon to the magnesium hydride to the ammonium borate to the rhamnose to the binder is controlled within a certain range. The soil conditioner provided by the invention is good in slow release performance, can release effective components durably and slowly, and has multiple effects of improving soil quality, providing nutrition, promoting crop growth, improving resistance and the like; the problems of soil acidification, heavy metal pollution, micro-plastic pollution, pesticide pollution, antibiotic pollution and the like in agricultural production can be effectively solved.

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 provides a chemical restoration method for treating heavy metal-polluted terrain soil by ferromagnetic artificial nucleuses. In a ferromagnetic artificial nucleus soil restoration method, the ferromagnetic artificial nucleuses are applied to heavy metal-polluted soil; soil above a heavy metal pollution depth is overturned by cultivation equipment to uniformly mix with the ferromagnetic artificial nucleuses; and after heavy metal can be dispersed from the soil to the artificial nucleuses by a period of time, the soil containing the artificial nucleuses is overturned by using the cultivation equipment to increase the change of the artificial nucleuses contacting with other soil. After the soil overturning procedure is repeated by multiple times, the cultivation equipment provided with magnets can be used for separating the ferromagnetic artificial nucleuses, absorbed with the heavy metal, from the polluted soil; and the content of the heavy metal of the polluted soil is reduced due to shifting-out of the ferromagnetic artificial nucleuses, so that the purpose of soil restoration is gradually achieved.

The invention provides a food heavy metal pollution detection system based on plasma emission spectrum analysis, and aims to realize rapid and accurate detection of heavy metal elements in a food sample. The system comprises a sample processing module, an excitation module, a spectrum detection module, a signal processing module, a data analysis module and an output module. The sample processing module converts a food sample into an aerosol or steam state. The spectrum detection module is used for extracting spectrum characteristic information of heavy metal elements. And the signal processing module adopts an AI-driven spectrum denoising algorithm to remove background noise and interference signals. And the data analysis module calculates the concentration of each element based on the characteristic peak intensity and generates heavy metal pollution data. And the output module is used for visually displaying the detection result. According to the method, complex sample pretreatment steps are reduced, the detection efficiency and sensitivity are improved, and the method is suitable for food safety detection scenes and particularly has remarkable advantages in field rapid detection application of agricultural products, drinking water and processed food.

The invention discloses a drainage basin water body heavy metal pollution prediction system based on multi-modal attention, and the system comprises a data collection module which is used for collecting multi-modal data related to drainage basin water body heavy metal pollution, and a preprocessing module which is used for carrying out the standardization processing of the obtained multi-modal data. The multi-modal attention fusion module is used for performing feature extraction and cross-modal interaction on the preprocessed multi-modal data to generate fusion features; the dynamic prediction module is used for outputting a spatial-temporal distribution prediction result of the heavy metal pollutant concentration in the drainage basin by constructing a spatial-temporal coupled prediction model; according to the method, the cross-modal interaction accuracy is improved by dynamically focusing the key association information of the multi-modal data through the intra-modal and inter-modal attention mechanism, meanwhile, the data-driven prediction model is constructed, the prediction precision of the high-risk area is optimized through the weighted loss function, the prediction error is effectively reduced, and the prediction efficiency is improved. And high-precision dynamic prediction of heavy metal pollution under the watershed scale is realized.

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 discloses a method for preparing a bismuth ferrite composite catalytic material by using red mud as well as a product and application of the bismuth ferrite composite catalytic material. The method comprises the following steps: mixing chlorine salt and red mud, uniformly stirring, adding a hydrochloric acid solution, uniformly stirring, roasting, recovering flue gas generated in the roasting process, and obtaining roasting residues and roasting dust generated by recovering the flue gas after roasting is finished; and mixing the roasted dust and bismuth nitrate, uniformly stirring, adding the calcined residue, uniformly grinding, adding water, stirring, drying the material, and calcining to obtain the bismuth ferrite composite catalytic material. The preparation process is simple, the red mud is used as a main raw material, and full-amount resource utilization of the red mud can be achieved. The prepared composite catalytic material can be used for efficiently purifying landfill leachate, and COD, ammonia nitrogen, total phosphorus and heavy metal pollution are efficiently removed through a photocatalysis mechanism.

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 multi-component heavy metal spectrum deep learning detection method and system, and belongs to the technical field of analytical chemistry and instrument analysis. According to the method, the UV-Vis spectral difference of multi-component heavy metals is enhanced through a combined chemical probe color development technology, complex nonlinear spectral signals are analyzed in combination with a deep learning model, and the generalization ability of the model is improved through a data enhancement strategy. The system is integrated with a lightweight end-to-end detection platform, is adaptive to low-cost portable equipment, and realizes minute-level multi-component synchronous detection. According to the method, a closed-loop technical path is formed through three core technologies (probe combination, spectrum enhancement and deep learning), the technical bottleneck of a traditional method in a multi-component, small-sample and complex scene is broken through, and an efficient, accurate and low-cost complete solution is provided for heavy metal pollution prevention and control; the method solves the problems of expensive equipment, low detection efficiency and insufficient multi-component analysis capability of a traditional method, and is suitable for scenes of environmental monitoring, sewage treatment and the like.

The invention discloses a method for preparing a catalyst by using red mud and titanium dioxide acidolysis waste residues as well as a product and application thereof. The method comprises the following steps: calcining red mud, adding titanium dioxide acidolysis waste residues and an auxiliary agent, uniformly stirring, adding a desulfurization waste liquid, uniformly stirring, carrying out a hydrothermal reaction, carrying out solid-liquid separation, and calcining to obtain the catalyst material. The preparation process is simple, and efficient resource utilization of the red mud and the titanium dioxide acidolysis waste residues can be achieved. The catalyst prepared through a hydrothermal reaction and a secondary calcination process realizes efficient removal of COD, ammonia nitrogen, total phosphorus and heavy metal pollutants in waste liquid through a photocatalytic function.

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 discloses a carbon / nano zero-valent iron composite material based on coal gasification slag and a preparation method of the carbon / nano zero-valent iron composite material. The method comprises the following steps: performing component separation on the coal gasification slag to obtain an iron-rich residual carbon component, mixing and grinding the iron-rich residual carbon component and sodium hydroxide according to a specific mass ratio, performing high-temperature calcination treatment in a nitrogen protective atmosphere, and performing hydrothermal activation reaction, solid-liquid separation, washing and drying to obtain the carbon / nano zero-valent iron composite material. The material shows excellent hexavalent chromium removal performance, can realize efficient removal of Cr (VI) in water within 5 minutes (the removal rate reaches 78.9%), and has remarkable structural stability. Synergistic activation of the endogenous iron-carbon components in the industrial solid waste is creatively achieved, a green and environment-friendly novel functional material solution is provided for heavy metal pollution treatment, and the method has important environmental benefits and industrial application value.

The invention provides a biochar composite porous material for heavy metal contaminated soil remediation and a preparation method and application of the biochar composite porous material. The preparation method comprises the steps that biomass raw materials are subjected to high-temperature pyrolysis carbonization and then smashed, ground and sieved, and biochar powder is obtained; dispersing in a phosphoric acid solution to obtain a biochar solution, and filtering, washing and drying after a hydrothermal activation reaction to obtain activated biochar; dispersing in a chitosan solution, carrying out ultrasonic oscillation, filtering, washing and drying to obtain modified charcoal; mixing sodium bentonite, attapulgite and deionized water, uniformly mixing the modified charcoal, the functional slurry and a sodium carboxymethyl cellulose solution to obtain composite slurry, performing compression molding to obtain a green body, and then drying and calcining the green body. The biochar composite porous material prepared by the invention can simultaneously play multiple roles of physical adsorption, chemical chelation, ion exchange and the like in a heavy metal contaminated soil treatment process, so that efficient fixation of various heavy metal ions is realized.

The invention discloses an adsorption-extraction combined heavy metal polluted water remediation system and method, and belongs to the field of water pollution treatment. The system is provided with a sensing unit which collects data in real time through a heavy metal concentration sensor and the like; the model unit outputs key information such as adsorbent real-time saturation and the like based on the acquired data and an adsorbent saturation prediction model; the decision-making unit is used for calculating a dynamic threshold value according to the information, accurately judging regeneration time and optimizing regeneration parameters; the execution unit executes a regeneration operation according to the instruction. And the feedback unit feeds back the regenerated data to the model unit to realize dynamic optimization of the system. According to the method, closed-loop operation of data acquisition, processing, decision making and feedback is realized based on cooperation of all units of the system. According to the invention, the remediation efficiency of the heavy metal polluted water body is effectively improved, the regeneration of the adsorbent is accurately controlled, the operation energy consumption is reduced, the complex and changeable pollution working conditions can be better handled, and the effluent quality is guaranteed.

The invention relates to the technical field of soil pollution detection, in particular to a soil pollution real-time detection and analysis method and system based on spectral analysis, and the system comprises a soil pollution detection platform, a soil database, a regional pollution division unit, a sample check analysis unit, a regional identification analysis unit, a pollution diffusion analysis unit and a display response unit. Analysis is carried out from two points of an unmanned aerial vehicle hyperspectral technology and a sample collection and detection technology, namely, regional pollution division feedback analysis is carried out on a spectral feature image to judge whether a heavy metal pollution area exists in a target soil area or not and the pollution degree condition, and regional pollution matching check analysis is carried out on sample parameters to judge whether the target soil area has a heavy metal pollution area or not. According to the method, the unmanned aerial vehicle hyperspectral detection can be checked and fed back through sample experiment detection, and whether the two types of detection are consistent or not can be visually known through an information feedback display mode, so that the availability of a detection result is ensured.

The invention discloses alkali-producing cadmium-tolerant acinetobacter capable of increasing the pH value of soil and application of the alkali-producing cadmium-tolerant acinetobacter, and belongs to the technical field of microorganisms, the alkali-producing cadmium-tolerant acinetobacter is preserved in the China General Microbiological Culture Collection Center (CGMCC), the number of the alkali-producing cadmium-tolerant acinetobacter is CGMCC No.33619, and the alkali-producing cadmium-tolerant acinetobacter belongs to proteobacteria and pseudomonas. The strain can increase the environmental pH value from 6.0 to 8.0 through metabolism, and stably grows in an environment with the cadmium (Cd < 2 + >) concentration as high as 20 ppm. The strain can be applied to the fields of acid soil improvement, cadmium pollution remediation, industrial wastewater treatment, fermentation engineering and the like. The method has the advantages of being environmentally friendly, efficient, stable, applicable to multiple scenes and the like, and a novel microbial resource is provided for heavy metal pollution treatment and soil ecological restoration.

The invention discloses an unconventional feed quality assessment method and system, and relates to the technical field of unconventional feed quality assessment, and the method comprises the following steps: in a feed high-temperature drying treatment process, establishing a multi-dimensional sensing monitoring baseline, continuously obtaining mercury vapor concentration gradient, moisture evaporation rate and airflow velocity field data in a feed drying environment, and calculating the quality of the feed; forming a time sequence feature set reflecting the dynamic change of mercury vaporization; and constructing a gas-solid interaction analytical model by using the time sequence feature set, and carrying out coupling analysis on the mercury vapor migration trajectory and the temperature distribution and humidity distribution of the feed particle surface to generate a spatial distribution data set representing the spatial distribution feature of the mercury vapor redeposition region. According to the invention, the whole-process monitoring and dynamic regulation and control of mercury vaporization, migration and redeposition in the feed drying process are realized, through multi-dimensional monitoring, amplitude limiting control and high-frequency disturbance, the mercury distribution is balanced, the risk is reduced, the drying safety and the feed quality stability are improved, and the controllable management of heavy metal pollution is effectively realized.

The invention relates to the technical field of soil remediation, in particular to a charcoal-fungus symbiont for a freeze-thaw environment and a preparation method and application of the charcoal-fungus symbiont. The preparation method of the charcoal-fungus symbiont comprises the following steps: crushing a biomass raw material, and carrying out pyrolysis to prepare basic charcoal; placing the basic biochar in an iron oxide solution, and carrying out an iron oxide loading reaction to obtain iron oxide biochar; the preparation method comprises the following steps: dipping iron oxide biochar in a chitosan solution for coating modification, then continuously adding a cross-linking agent for cross-linking solidification, and freeze-drying to obtain chitosan-iron oxide composite modified biochar; and immersing the chitosan-iron oxide composite modified biochar into a bacterial suspension of functional microorganisms, oscillating and adsorbing, and freeze-drying to obtain the carbon-bacteria symbiont. The invention aims to improve the heavy metal pollution phenomenon of soil and the soybean root rot disaster in the freezing and thawing cycle environment at the same time.

The invention discloses a method for improving cadmium accumulation and transport capacity of sedum plumbizincicola, and relates to the field of plant heavy metal pollution remediation. The method comprises the following steps: preparing a selenium solution with the selenium ion concentration of 5 [mu] M, adding the selenium solution into the cadmium-polluted water environment, planting sedum plumbizincicola seedlings in the cadmium-polluted water environment, and carrying out cultivation management; plant pretreatment: culturing sedum plumbizincicola seedlings in non-polluted soil for at least three generations, selecting sedum plumbizincicola plants which are uniform in size and healthy in growth vigor, and pre-culturing the sedum plumbizincicola plants in a Hoaglands nutrient solution for two weeks for rooting; the healthy plants are transferred into Hoagland nutrient solution containing selenium solution to be cultured, and the plants are harvested after 28 days. According to the method, the high-concentration cadmium polluted water environment is simulated, and selenium is applied in the growth period of the sedum plumbizincicola, so that absorption and enrichment of the sedum plumbizincicola on cadmium are promoted, the plant biomass is improved, the toxic effect of cadmium on plants is relieved, and efficient remediation of the sedum plumbizincicola on the cadmium polluted environment is achieved.

The invention discloses a numerical prediction method and system for heavy metal pollution load evolution in a drainage basin, and belongs to the technical field of drainage basin heavy metal pollution prevention. According to an existing drainage basin pollution prediction scheme, the influence of heavy metal pollution leaching from soil due to rainfall and the influence of atmospheric heavy metal pollution sedimentation are not considered, so that the conversion and transportation processes of heavy metal pollution cannot be accurately simulated. According to the numerical prediction method for heavy metal pollution load evolution in the drainage basin, heavy metal multi-dimensional evolution data is obtained by constructing a heavy metal parameter analysis model, a heavy metal three-dimensional evolution model and a heavy metal coupling prediction model; then the heavy metal multi-dimensional evolution data, weather data and hydrological calculation data are subjected to coupling calculation to obtain an accurate heavy metal pollution prediction value, so that the evolution process of heavy metal pollution between water, land and air can be accurately simulated; and the development trend of the heavy metal pollution load in the drainage basin can be tracked and predicted more comprehensively.