2013 results about "Heavy metals pollution" 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 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 comprehensive evaluation method and system for the remediation effect of heavy metal contaminated soil, and relates to the technical field of environmental engineering.The method specifically comprises the steps that remote sensing data of a contaminated area is obtained, a soil sample is collected, the microbial respiration rate and the soil physicochemical property of the soil sample are measured, the remote sensing data are processed, and a normalized vegetation index is obtained; generating a soil health assessment index based on the collected physical and chemical properties of the soil; integrating the soil evaluation index, the microbial respiration rate and the normalized vegetation index to construct a soil remediation evaluation index; and comparing the remediation evaluation index with a preset evaluation threshold value, and evaluating the remediation effect of the heavy metal contaminated soil. According to the method, the soil health evaluation index and the remediation evaluation index are constructed, so that the evaluation accuracy of the remediation effect of the heavy metal contaminated soil is effectively improved. Through multivariate correlation analysis, biological, chemical and physical characteristics of the soil can be comprehensively reflected, and a scientific basis is provided for subsequent management and remediation measures.

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 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 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 relates to the technical field of environmental remediation, and discloses a biochemical synergistic remediation method for heavy metal contaminated soil, which comprises the following steps: S1, sampling the heavy metal contaminated soil, and analyzing the type, concentration and occurrence form of heavy metals of the heavy metal contaminated soil; s2, extracting active ingredients of polyphenols, organic acids or alkaloids from the plant materials; s3, applying a plant source activating agent to regulate the chemical form of the heavy metal in the soil and promote the activation of the heavy metal; s4, inoculating a functional microbial agent to enhance the heavy metal immobilization, transformation and phytoremediation capabilities; s5, planting hyperaccumulators, and cooperatively using the plant extract, the plant source activator and the microbial agent; s6, plant residue treatment and soil improvement. Through the synergistic effect of the plant extract, the plant source activator, the microbial agent and the hyperaccumulator, the plant source activator, the microbial agent and the hyperaccumulator can jointly act on the heavy metal contaminated soil from multiple ways, and chelating, activating, degrading and fixing of heavy metal are rapidly achieved.

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 method for preparing a waste liquid purification material from electrolytic manganese residues and phosphogypsum leachate and the waste liquid purification material, and belongs to the technical field of hazardous waste resource utilization. The method comprises the steps that the electrolytic manganese residues and the iron-containing solid waste are mixed and stirred to be uniform, and iron-manganese residues are obtained; mixing the iron-manganese slag with fly ash and pug, uniformly stirring, granulating and aging to obtain raw material granules; the method comprises the following steps: mixing the landfill leachate and the ardealite leachate, and uniformly stirring to obtain a hydrothermal solution; immersing the raw material particles in a hydrothermal solution, and carrying out a hydrothermal reaction to obtain an initial purified material; and carbonizing the initial purification material to obtain the waste liquid purification material. The preparation process is simple, the required raw materials are all industrial wastes, the waste liquid purification material is prepared from the electrolytic manganese residues and the ardealite leachate through reasonable proportioning, hydrothermal reaction and carbonization, high-concentration organic pollutants in the waste liquid can be efficiently adsorbed and degraded, and heavy metal pollutants in the waste liquid can be synchronously removed.

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 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 rice field soil conditioner which is prepared by mixing a modified zeolite molecular sieve, a biological fertilizer, polymerized bentonite, biochar and nano zero-valent iron according to the mass ratio of (20-25): (30-35): (25-30): (15-20): (5-8), by adding the modified zeolite molecular sieve and the polymerized bentonite, fertilization nutrients in soil can be effectively adsorbed, the nutrient loss speed is greatly reduced, the soil nutrient retention rate is increased, the use of chemical fertilizers is reduced, and sustainable agricultural development is facilitated; the modified zeolite molecular sieve can absorb water at the same time and can release water in high-temperature weather, so that the soil structure is effectively improved, the water-retaining property of rice field soil is improved, and the phenomenon that planted rice is withered due to the fact that water is not supplemented in time in hot weather is avoided; and meanwhile, the combination of the nano zero-valent iron and the biochar can adsorb heavy metal ions in the soil and carry out reduction fixation, so that the heavy metal pollution in the rice field soil is reduced.

The invention belongs to the technical field of agricultural fertilizers, and provides a soil remediation fertilizer and a preparation method thereof. The preparation method comprises the following steps: S1, sequentially drying and crushing a natural organic matter material to obtain a pretreated natural organic matter material; s2, mixing the pretreated natural organic matter material, microbial flora, a plant growth regulator, a stabilizer and a slow release agent, and performing fermentation treatment to obtain a compound fertilizer; s3, sequentially drying, granulating and packaging the compound fertilizer to obtain the soil remediation fertilizer. The soil remediation fertilizer provided by the invention can effectively solve the remediation problem of organic matter and heavy metal contaminated soil, improve the soil quality and promote plant growth, and has remarkable environmental protection and agricultural sustainable development benefits.

The invention belongs to the technical field of microorganisms, and particularly relates to a manganese-resistant manganese oxide strain Klebsiella oxytoca YJ and application of the manganese-resistant manganese oxide strain Klebsiella oxytoca YJ. The invention discloses a manganese-resistant manganese oxide strain YJ, the taxonomic name of the manganese-resistant manganese oxide strain YJ is Klebsiella oxytoca, the manganese-resistant manganese oxide strain YJ is preserved in Guangdong Microbial Culture Collection Center on May 21, 2024, and the preservation number is GDMCC No: 64667. According to the invention, manganese-resistant strains are screened to obtain a strain YJ of which the tolerance concentration to manganese is 200mM, and the strain YJ is Klebsiella oxytoca through morphological and molecular biological identification, and the strain YJ is Klebsiella oxytoca. Research on the removal rate of heavy metals shows that the removal rate of Mn (II) in a 500mg / kg culture solution reaches 92.20% at the 4th day, the removal rate of As (III) in a 50mg / kg culture solution reaches 98.40% at the 4th day, and the removal rate of Cd (II) in a 5mg / kg culture solution reaches 57.07% at the 5th day. Therefore, the strain YJ has very important significance on pollution treatment of heavy metals such as Mn, As and Cd.

The invention discloses a flood area heavy metal pollution source identification and risk assessment system based on machine learning, and relates to the technical field of heavy metal pollution. Flood prediction is carried out through the environment data; extracting flood circulation data of the target area from the flood prediction data, and determining a heavy metal pollution source; performing diffusion analysis on the heavy metal pollution source to obtain the heavy metal concentration of the flood in the target area; taking the heavy metal concentration of the flood in the target area as an input parameter of a pre-trained machine learning model to obtain a heavy metal variation of soil in the target area; and performing risk assessment according to the original heavy metal concentration and the heavy metal variation of the soil in the target area to obtain a risk level of the target area. According to the method, the heavy metal pollution range and intensity under the influence of flood can be recognized in advance, so that decision basis is provided for managers, resources are allocated in advance, and the influence of flood and heavy metal pollution caused by flood on the environment and residents is reduced.

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 contaminated soil remediation, in particular to a heavy metal contaminated soil remediation composition and a preparation method and application thereof. The heavy metal contaminated soil remediation composition is composed of a microbial agent for heavy metal treatment and a Fe3O4 (at) TiO2 (at) C material, and the microbial agent for heavy metal treatment is loaded on the Fe3O4 (at) TiO2 (at) C material. According to the composition, physical remediation, chemical remediation and biological remediation can be conducted at the same time, Fe3O4 and C are adopted to generate a physical adsorption effect on heavy metal ions, so that the heavy metal ions are enriched on the composition, Fe3O4 and TiO2 and C are adopted to conduct chemical photocatalytic degradation on the heavy metal ions, then microorganisms are adopted to conduct conversion on the heavy metal ions, and therefore the heavy metal ions can be effectively degraded. Efficient treatment of soil heavy metal ions is achieved through the composition.

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.